# Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones

Ismail Amessegher, Arthur Gaudard, Kojo Nyamekye Anyinam-Boateng, Hugo Le Blevec, Lionel Génevé, Florian Pouthier, Mathieu Leonardon, Hajer Fradi, Lucia Bergantin, Panagiotis Papadakis, et al.

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## To cite this version:

Ismail Amessegher, Arthur Gaudard, Kojo Nyamekye Anyinam-Boateng, Hugo Le Blevec, Lionel Génevé, et al.. Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones. IEEE International Symposium on Safety, Security, and Rescue Robotics, Oct 2025, Galway, Ireland. hal-05280264

HAL Id: hal-05280264

https://imt-atlantique.hal.science/hal-05280264v1

Submitted on 15 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Towards Low-Latency Object Detection on Board Reactive Search-and-Rescue Drones

Ismail Amessegher<fnref n="1" />, Arthur Gaudard<fnref n="1" />, Kojo Nyamekye Anyinam-Boateng<fnref n="2" />, Hugo Le Blevec<fnref n="1" />, Lionel Génevé<fnref n="2" />, Florian Pouthier<fnref n="2" />, Mathieu Léonardon<fnref n="1" />, Hajer Fradi<fnref n="1" />, Lucia Bergantin<fnref n="1" />, Panagiotis Papadakis<fnref n="1" />, Isabelle Fantoni<fnref n="2" />, Jean-Philippe Diguet<fnref n="3" />, and Matthieu Arzel<fnref n="1" />

**Abstract**—Drones play a crucial role in search-and-rescue missions by providing real-time information on areas of interest that are difficult to access or endangering to human rescuers. However, analyzing raw video feeds by human operators to detect objects of interest, such as vehicles or victims, becomes increasingly demanding as the mission duration increases. This underscores the need for embedded computer vision to reduce the operator's cognitive load and enhance mission responsiveness. Towards this goal, we propose a low-latency object detection model based on YOLO, fitted to search-and-rescue missions, and able to process data coming from RGB and event cameras. We also propose a low-latency implementation on FPGA on board drones, achieving accurate detection in less than 20ms. Through a series of tests using a prototype drone, we highlight the features of the model and processing core that favor drone reactivity and operational autonomy.

## I. INTRODUCTION

Unmanned aerial vehicles (UAVs) are excellent tools for search and rescue missions, providing a quick overview of disaster scenes that enables more secure and efficient deployment of human teams. Straightforward UAV teleoperation relies mostly on the operator's ability to analyze a video feed searching for humans or vehicles in devastated areas while controlling the drone, which becomes increasingly challenging as mission duration increases. This motivates the use of computer vision-based environment perception to assist the operator. For example, in the task of human detection [1], a UAV may automatically initiate a static flight procedure over a detected human. Detection models can be optimized for Search-And-Rescue (SAR) environments [2] and achieve frame rates beyond 65 frames per second (FPS), but require wireless access to a distant powerful GPU (a V100 in [2]). In such a case, the cumulated time that is required for capturing images, buffering, and transmitting over the air to distant processing and finally warning the operator or triggering an automatic maneuver, can be prohibitive or

compromising to the mission objectives, even though the distant GPU processes the video feed at a high frame rate. Therefore, onboard computer vision is a major asset for SAR drones to increase their reactivity.

Different solutions leverage deep learning techniques and low-power boards based on embedded GPUs or FPGAs [3] to integrate computer vision capabilities onto UAVs. This is challenging as these platforms face strong Size, Weight and Power (SWaP) constraints. FPGAs, in particular, allow highly optimized execution of Deep Learning models with very low power consumption [4]. This has enabled successful implementations of Convolutional Neural Networks (CNNs) for tasks such as human, vehicle, or wildfire detection in FPGA-equipped UAVs [5], [6], [7]. However, the main focus has so far been on low-power processing and low computing and memory resources, while the sensing-and-processing latency is relatively overlooked.

SwiftEagle [8] is an example of a low-latency UAV open-source platform, based on FPGA processing of data jointly provided by RGB and event cameras. It delivers data to an FPGA processing unit within 210µs upon event camera stimulation. This type of camera provides lower latency than conventional RGB cameras (in the order of µs vs. a few ms). Thanks to this extremely low latency in sensing and processing, this platform can autonomously avoid obstacles at high speed [9], triggering an evasion maneuver in less than 3ms. However, it does not deal with object detection, a more demanding task that requires the processing of high-resolution images with algorithms such as Region-based CNN (R-CNN), Single Shot Detector (SSD), or You Only Look Once (YOLO). A YOLOv3 detector was implemented on an FPGA-based UAV [4] for SAR, but with no effort to reduce the latency, thus requiring 469ms per inference.

Our work aims to show that this object detection task can be efficiently processed on board a UAV, leveraging an FPGA platform with event and/or RGB cameras. This approach achieves an inference latency around 6ms with a resolution of 640×480, comparable to the obstacle-avoidance latency of SwiftEagle with a resolution of 80×80, while operating within the UAVs limited computing and energy resources. To achieve this, we propose *Lola-YOLO*, a computationally efficient YOLO model specifically designed to be compatible with RGB and event cameras. In contrast to [4] and [8], where a Deep-learning Processing Unit (DPU) is employed,

*This work was supported by Labex CominLabs excellence laboratory managed by the French National Research Agency in the "Investing for the Future" program under reference ANR-10-LABX-0007-01, project LEASARD, and by the French National Research Agency project VORTEX 2023-28, under reference ANR-23-IAS2-0005.

<footnote n="1">¹IMT Atlantique, Lab-STICC, UMR CNRS 6285, 29238 Brest, France. `firstname.lastname@imt-atlantique.fr`</footnote>

<footnote n="2">²Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, France. `firstname.lastname@ls2n.fr`</footnote>

<footnote n="3">³CNRS, IRL 2010 CROSSING Adelaide, Australia. `jean-philippe.diguet@cnrs.fr`</footnote>
we reside on an efficient FPGA-based implementation using FINN [10]. While DPUs offer easy and flexible CNN accelerators that iteratively run layer computations on multiplier arrays, they are not as efficient as FINN-based accelerators that pipeline layer computations on cascaded parallel processing units. Thus, FINN provides an accessible framework that facilitates the deployment of low-latency CNN accelerators on FPGAs, paving the way for highly responsive drones.

To address the aforementioned challenges, this work advances the state-of-the-art in the following directions:

* We develop *LoLa-YOLO*: a **Low-Latency** detection model, based on evolutions of **YOLO** [13], optimized for FPGA implementation. To our knowledge, this is the first low-complexity YOLO model destined for detection in SAR scenarios using event or RGB cameras. Other low-complexity YOLO models exist, as LPYOLO [14], but they are suited for tasks such as face detection and are not trained on event-based datasets.

* We report prototype measurements that confirm the low latency and low power consumption of our solution.

* Through indoor tests with a UAV-mounted event camera flying over miniaturized objects, we demonstrate the potential of *LoLa-YOLO* even though it was trained on automotive and city-scape datasets.

## II. DESIGN AND TRAINING OF LOLA-YOLO

### A. Design of LoLa-YOLO, a low-latency YOLO for UAVs

Our study is based on YOLOv3-tiny as a baseline [15], designed to fit embedded systems with simple convolutional layers and two-scale prediction, as opposed to the original YOLOv3 with residual convolutional layers and three-scale prediction [16], yielding a good trade-off between accuracy and computation cost. Indeed, a SAR UAV will not fly close to the objects to detect, and thus will not need the largest scale detection head of YOLOv3. More recent versions of YOLO also exist, the latest being YOLOv13 [17], which improves the detection accuracy over YOLOv12 and YOLOv11, but not the latency, YOLOv11n being still faster than YOLOv13-N. YOLO versions 3 to 12 are compared in [18]. This paper shows that YOLOv3-tiny is the best choice for latency-sensitive applications, as it delivers the fastest inference on GPU, outperforming YOLOv11n. [19] compares implementations of different YOLO versions on FPGA and also shows that the versions beyond YOLOv3 suffer from higher latencies. When considering implementation on hardware, the cost of extensive adaptations of the framework has also to be considered, as shown in [20]. YOLOv3 fits well with frameworks such as FINN, and adapting them to the latest versions of YOLO consumes considerable engineering time for a minor improvement in accuracy and a loss in latency. Still, for YOLOv3-tiny with 8.6M parameters in total, there is a significant margin for further optimization on latency in SAR applications, which motivates the development of *LoLa-YOLO* (see Fig. 1), destined to process data from both event and RGB cameras. Even though we advocate for event cameras for low latency

applications, RGB cameras are deployed on most of the drones and will benefit from faster processing. We selected a 640 × 480 resolution as a conventional use case for both types of cameras.

*LoLa-YOLO* shares part of the backbone of YOLOv3-tiny but with a last layer, the neck, and with detection heads that were simplified to be more adapted to our task of detecting cars and humans. In fact, YOLOv3-tiny was trained in MS-COCO [21] and Pascal-VOC [22] that contain multiple classes (80 and 20, respectively) and objects seen at very different distances and from viewpoints that would be different from those encountered by SAR UAVs. In particular, YOLOv3 detection heads are oversized with feature maps of dimensions up to 1024 × 20 × 20 when we could achieve similar accuracy on fewer classes (humans, cars, boats, for instance) with less convolutional layers (10 instead of 11 for YOLOv3-tiny) before the last detection layers and with dimensions up to only 195 × 20 × 20. Thanks to these modifications, *LoLa-YOLO* requires only 667k parameters, dividing the memory footprint by 13 and also reducing the computational cost as shown in Section IV, allowing a lower latency, while achieving good accuracy as described hereafter.

### B. Training with event and RGB-based datasets

*LoLa-YOLO* is designed to deal with events and RGB frames. To do so, frames are built by accumulating events during a given time interval, depending on the target frame rate. Although there is no dataset for event-based object detection in SAR environments, detecting cars and humans is a typical task in fire and flooding situations in city environments, and there are some datasets for event-based detection of humans and cars in cities at different altitudes, namely DSEC-MOD (event+RGB) [11] and eTraM (event only) [12]. Therefore, we choose to evaluate *LoLa-YOLO* on this task with these two datasets that allow us to train our model for different static viewpoints (horizontal with DSEC-MOD and at 6 m with a pitch angle of about 35° to the ground with eTraM) with events and RGB frames. eTraM is also of high interest thanks to the various conditions it covers: different locations, during daytime, nighttime, and twilight, under different weather conditions, including sunny, overcast, and rainy. A UAV may, in practice, observe a disaster scene from such viewpoints and diversity in conditions, but also at higher heights and not only in cities. This could be improved with event-based datasets built to train models for SAR conditions, though it would require extensive data collection and labeling efforts. A first alternative would be to train on synthetic event-based datasets that could be built with realistic simulation environments to mimic varying SAR situations, similar to what was proposed in [23]. The domain gap would thus be reduced, even though it does not seem critical, as shown by our experiments reported in Section IV.

Initially, we train a floating-point (FP32) version of *LoLa-YOLO* as a baseline reference. The accuracy is measured using the mean Average Precision at an Intersection over Union (IoU) threshold of 0.5, commonly referred to as
<figure><img src="image_3.png" /><figcaption>Fig. 1: Architecture of *LoLa-YOLO* (c) adapted to 640 × 480 frames, built with cumulated events from an event camera or provided by an RGB one, trained on DSEC-MOD [11] (a) and eTraM [12] (b) datasets.</figcaption></figure>

<table><caption>TABLE I: Compared accuracies of FP32 and quantized versions of *YOLOv3-tiny* and *LoLa-YOLO* on DSEC-MOD [11] and eTraM [12] datasets, with FPGA (AMD ZU19EG) resource utilization using FINN [10].</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Quantization</th><th colspan="2">DSEC-MOD mAP@0.5</th><th>eTraM (day)</th><th colspan="5">FPGA Resource Utilization</th></tr><tr><th>Event</th><th>RGB</th><th>mAP@0.5:0.95</th><th>LUT</th><th>FF</th><th>BRAM</th><th>URAM</th><th>DSP</th></tr></thead><tbody><tr><td rowspan="3">YOLOv3-tiny</td><td>FP32</td><td>0.55</td><td>0.62</td><td>0.42</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>INT8</td><td>0.54</td><td>0.62</td><td>0.41</td><td>432 581</td><td>232 130</td><td><b>1526</b>*</td><td>3</td><td>1426</td></tr><tr><td>INT4</td><td>0.54</td><td>0.62</td><td>0.40</td><td>126 131</td><td>128 793</td><td>830</td><td>7</td><td>834</td></tr><tr><td rowspan="3">LoLa-YOLO</td><td>FP32</td><td>0.52</td><td>0.58</td><td>0.41</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>INT8</td><td>0.51</td><td>0.58</td><td>0.41</td><td>124 193</td><td>140 538</td><td>518</td><td>5</td><td>643</td></tr><tr><td>INT4</td><td>0.51</td><td>0.58</td><td>0.40</td><td>83 277</td><td>90 193</td><td>172</td><td>5</td><td>463</td></tr></tbody></table>

mAP@0.5 for DSEC-MOD and the average mAP@0.5:0.95 from MS-COCO for eTraM. Then, we apply Quantization Aware Training (QAT) to quantize the model to INT8 and INT4 integer precision levels, evaluating each version's performance. Overall, INT8 quantization helps balance the trade-off between model accuracy and computational efficiency for embedded GPUs as NVIDIA Jetson Orin Nano. Nevertheless, embedded GPUs are not designed to optimize operations with formats smaller than INT8, as can be done with FPGAs that can be configured for any quantization.

We first use the training parameters reported in [24] for a YOLOv7 achieving state-of-the-art accuracy for DSEC-MOD and then run an extensive hyperparameter search for *YOLOv3-tiny* and *LoLa-YOLO*. The results of the final training are summarized in Table I. *LoLa-YOLO* achieves mAP@0.5=0.52 on events and 0.58 on RGB frames with DSEC-MOD so that it surpasses the performance of the reference YOLOv7, i.e. mAP@0.5=0.55 for RGB, 0.39 for events, without fusion [24]. *LoLa-YOLO* achieves mAP@0.5:0.95=0.40 with eTraM (day, all classes), which is less than the 0.572 of the complex Recurrent Vision Transformers [25] and far better than the 0.188 of YOLOv8 [12]. The appropriate choice of hyperparameters significantly improves model accuracy, even when using the same architecture and dataset. As illustrated in Fig.2 for *LoLa-YOLO*, we observe an improvement of 9% in mAP@0.5 by adjusting the training parameters beyond what was originally proposed [16], [24]. Specifically, reducing the learning rate

<figure><img src="image_4.png" /><figcaption>Fig. 2: mAP@0.5 of *LoLa-YOLO* for training with different hyperparameters on events with DSEC-MOD.</figcaption></figure>

helps for more stable learning along epochs. Additionally, decreasing both the number of warm-up epochs and the warm-up bias learning rate helps prevent overly aggressive starts in the first epochs, which can lead to a drop in accuracy over training. Another important parameter is momentum; a slight increase leads to a smoother gradient descent, reducing the sensitivity to minor local variations. By applying these changes, the model learns less per epoch but achieves greater overall improvement, especially during long training. The performance of YOLOv7 [24] and YOLOv8 [12] could also be improved with a similar effort. However, this confirms our initial intuition that a simple model with a two-stage prediction and a simple neck is well-sized for our detection task, but under the condition of finely tuned training<fnref n="1" />.

<footnote>* more than available on the device, an AMD ZU19EG.</footnote>

<footnote n="1">¹Our model and training procedure are available at https://github.com/iamessegher/lola_yolo.</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 3: FINN-based Software-Hardware architecture implementing *LoLa-YOLO*, able to process data coming from event or RGB cameras.</figcaption></figure>

### III. IMPLEMENTATION OF LOLa-YOLO ON FPGA

Many solutions exist to implement a CNN accelerator on FPGA and thus achieve low-latency processing. The lowest latency is achieved with a highly optimized implementation as in [26], [27], which requires advanced skills in digital circuit design and long engineering. An easier way is to use frameworks such as VitisAI [28] or FINN to deploy an accelerator based on an ONNX description. We favor FINN rather than VitisAI since it provides a pipelined architecture with better performance in terms of latency and throughput, at the cost of a higher resource utilization. The quantized versions of *LoLa-YOLO* are exported to the ONNX format. Then, FINN transforms them into architectures optimized for the FPGA fabric available on the System on Chip (SoC) of our prototyping board. This SoC provides a CPU connected to a RAM and an FPGA fabric. As described in Fig. 3, the CPU collects the data from the camera, directly as frames with an RGB camera or as accumulated events to build frames with an event camera (1). Then, it uses a Direct Memory Access (DMA) (2) to feed the *LoLa-YOLO* detector, mostly implemented on the FPGA fabric with FINN (3). Feature maps are sent back to the CPU via DMA (4) for prediction and Non Max Suppression (NMS), to select the best bounding boxes (5). Since this post-processing accounts for less than 1% of the computing cost during CPU inference, its FPGA implementation was not a priority for our investigations.

### IV. EXPERIMENTS AND RESULTS

#### A. UAV experiments

Experiments were conducted using a UAV prototype, as illustrated in Fig. 4, within an indoor flight arena, equipped with toy cars and various carpet textures to evaluate different background conditions. As shown in Fig. 5b, our indoor tests validated the efficient detection by the floating-point version of *LoLa-YOLO* at different heights, with different angles of view and light conditions. We also obtained a straightforward

<figure><img src="image_6.png" /><figcaption>Fig. 4: Our UAV prototype, made up of a Crazy2fly quadcopter frame, a Pixhawk flight controller, an NVIDIA Jetson Orin Nano GPU board, and an Invention DVXplorer Micro event camera, with a 6mm focal length lens.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(a) Indoor flight tests using toy cars (13 cm in length) at altitudes of 30–100 cm, simulating outdoor flights above full-sized cars at altitudes of 10–30 meters.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(b) Detections with *LoLa-YOLO* on board our UAV prototype flying above toy cars in our indoor arena at different heights (between 30 and 50 cm).</figcaption></figure>

<caption>Fig. 5: Indoor UAV experiments.</caption>
<figure><img src="image_9.png" /><figcaption>Fig. 6: Outdoor detection with *LoLa-YOLO*, trained with eTraM, from a height of 6 meters, in front of our laboratory building, during a cloudy day (almost no wind). A video is available at https://youtu.be/OiB0UMzM5M4.</figcaption></figure>

detection outdoors, in front of our building, as illustrated in Fig. 6, without any adaptation of our model trained with eTraM. Our experimental setup, including the location, environment, and camera (an Inivation DVXplorer Micro rather than the Prophesee EVK4 HD used for the eTraM dataset), differs significantly from the original conditions. This experiment provides valuable insight into the impact of domain gap on performance in realistic SAR scenarios. A human lying on the ground was not detected, since there is no such situation in eTraM and DSEC-MOD, but humans and especially a moving car (the most prevalent class in eTraM) were correctly detected. Although not perfect, the successful detections obtained during this experiment confirm that our eTraM-dataset-trained model effectively adapted to real-world conditions and that the domain gap is not critical. This could be addressed by fine-tuning with more diverse datasets, containing airborne photographs as well as highly-realistic synthetic images as suggested in [23].

Building a platform similar to SwiftEagle [8] requires non-trivial engineering and testing *LoLa-YOLO* first onboard an NVIDIA Jetson Orin Nano GPU board was considered as a cautious choice. We replicated the measurement procedure given in [29] and we compared the onboard performance of *LoLa-YOLO* with YOLOv3-tiny and the state-of-the-art lightweight model YOLOv11n. These models are implemented on the GPU with TensorRT, running the inference in FP16 format instead of FP32, used for training, to reduce the inference time and power consumption. Table II sums up the measurements performed on the Jetson Orin Nano for the three models. The mean inference time (MIT) is measured as the host-GPU-host average latency (not taking into account the access to the data from the camera) by performing inferences on 5000 640×640 frames. The average power consumption (PC) of the model inferences is measured, and the energy per inference (EPI) is computed from the MIT and the PC. EPI provides a better insight into how effectively the model utilizes the limited energy for inference. *LoLa-YOLO* significantly reduces the latency of detecting objects with an MIT of 3.76ms per inference, outperforming YOLOv3-tiny and YOLOv11n (5.22ms and 7.22ms, respectively). Although the PC of *LoLa-YOLO* is higher than that of YOLOv11n, the lower latency of *LoLa-YOLO* results in a lower EPI (25.0mJ/inf. compared to

<table><caption>TABLE II: Comparison of the performances of FP16 versions of YOLOv11n, YOLOv3-tiny, and *LoLa-YOLO* on the Jetson Orin Nano embedded in the UAV prototype.</caption><thead><tr><th>Model</th><th>MIT (ms)</th><th>PC (W)</th><th>EPI (mJ/inf.)</th></tr></thead><tbody><tr><td>YOLOv11n</td><td>7.32</td><td><strong>5.04</strong></td><td>36.9</td></tr><tr><td>YOLOv3-tiny</td><td>5.64</td><td>6.79</td><td>38.3</td></tr><tr><td>LoLa-YOLO</td><td><strong>3.76</strong></td><td>6.66</td><td><strong>25.0</strong></td></tr></tbody></table>

36.9mJ/inf. for YOLOv11n). This further confirms the results provided in [18], namely, that YOLOv11n is not the best option when low latency and limited energy are major concerns over refined accuracy. In such a case, YOLOv3-tiny and its derivatives, as *LoLa-YOLO*, are favored.

## B. Inference on FPGA

<figure><img src="image_10.png" /><figcaption>Fig. 7: Measured latency for each stage of the FINN pipeline (batch size = 1) implementing *LoLa-YOLO*.</figcaption></figure>

As explained in [8], CPU/GPU-based systems are not as suitable as FPGA-based ones to design low-latency systems with strong requirements for sensor synchronization, although they can achieve low latencies for CNN tasks. Therefore, we opted for evaluating our implementation of *LoLa-YOLO* on FPGA. We evaluated the INT4 version operating at 300MHz with a batch size of 1 on the ZU19EG SoC. Throughput may be improved by increasing the batch size, but batch processing increases latency, since data must be buffered before being processed in parallel. Depending on the operational needs, a trade-off may be found between frame throughput and latency. The utilization of FPGA resources, detailed in Table I, shows that *LoLa-YOLO* requires fewer resources than YOLOv3-tiny, especially in terms of memory (1/4.8) and multipliers (1/1.8), leaving room for more processing on board the UAV. We measured a baseline power consumption of 18.7W for the SoC, which is required to run the operating system and power all the hardware resources. Then, we measured a total power consumption of 19.3W when running a whole inference with *LoLa-YOLO*. Thus, only 0.6W was used for *LoLa-YOLO*.

The latency, i.e. the time from an event received by the SoC to a decision, was measured by accounting for the different processing steps (see Fig. 7). Although the FINN implementation of *LoLa-YOLO* is straightforward compared to custom implementations [26], [27], it achieves a low latency of 6.07ms. 43% of the total latency is due to the postprocessing running on CPU, while it requires only 1% of the total computing time when running the whole processing on CPU. As shown in [30], implementing the NMS post-
processing on FPGA could reduce its latency to around 1ms, achieving a total latency below 10ms.

By combining the measured inference runtime and power consumption, we can conclude that *LoLa-YOLO* requires only 11.2mJ per inference on a SoC with FPGA.

## V. CONCLUSION

We introduced *LoLa-YOLO*, a low-latency object detection model compatible with event and RGB cameras and onboard processing to enhance the operational capabilities of SAR drones. By strategically modifying YOLOv3-tiny to meet the specific needs of SAR UAVs, we significantly reduced computational complexity and memory footprint, enabling deployment on resource-constrained FPGAs and embedded GPUs. Our experiments show that *LoLa-YOLO* outperforms the alternatives reported in the literature on two event-based datasets, DSEC-MOD and eTraM, although *LoLa-YOLO* was tailored for low-latency embedded processing. Measurements also demonstrated that *LoLa-YOLO* can achieve a detection latency below 10ms on board a UAV based on FPGA, proving that UAVs can benefit from efficient computer vision to gain in autonomy and reactivity. Testing *LoLa-YOLO* on a UAV demonstrated its ability to adapt effectively to conditions different from those in the training datasets. This could be improved with event-based datasets built to train models for SAR conditions.

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