dataesr/scientific-paragraphs-categorization huggingface dataset:
This relatively limited interest could be explained by the fact that, although YOLO detectors could be seen as a first choice for real-time applications in computer vision, they provide much lower detection accuracy when compared to region-based detectors, notably in detecting small objects. In  [51] , UAV-YOLO was proposed to adapt the YOLOv3 to detect small objects from unmanned aerial vehicle (UAV) data. Slight modification from YOLOv3 was done by concatenating two residual blocks of the network backbone having the same size. The authors actually focused more on training optimization of their dataset with UAV-viewed perspectives. They reported superior performance when compared to YOLOv3 and SSD models. In  [52] , the authors exploited YOLOv3 without any modification and showed that its performance in computational time is much better than Faster R-CNN or SSD.
We report this accuracy/time trade-off later in the experimental study. input (512×512) Table  1  provides a comparison of YOLO-fine with the three YOLO versions from the literature. We can see that our YOLO-fine model contains significantly fewer layers than YOLOv3 (68 versus 103) due to the removal of two final convolution blocks of Darknet-53 as mentioned above. Although our model is deeper than YOLOv1 and YOLOv2 (31 layers each), it provides multilevel detection, as in YOLOv3. More importantly, the size of the weight file remains the smallest with only 18 MB, as compared to 237 MB for YOLOv3 and even higher for the two older versions. This weight file size depends on the number of convolution filters of each layer more than on the number of layers. The size of the input image is set to 512 × 512 pixels, since this choice allow for us to work with experimental datasets that will be described in Section 4.
Experimental Study In this section, we first provide details of the public datasets used in our experiments, namely VEDAI (color and infrared versions)  [31] , MUNICH  [32]  and XVIEW  [33]. The first two datasets are made of aerial images while the last one contains satellite images. Next, we describe the experimental setup and recall the standard evaluation metrics used in our study. We then show and discuss the detection accuracy obtained by our model as compared against its counterparts, as well as provide a comparison on their performance in terms of storage requirement and computational time. More interestingly, we also set up and study the effect of new backgrounds appearing in validation sets.
VEDAI The VEDAI aerial image dataset  [31]  serves as a reference public source for multiple small-vehicle detection under various environments. The objects in VEDAI, in addition to being small, present different variabilities, such as multiple orientations, changes in lighting, shading, and occlusions. Additionally, different types of background can be found in this database, including urban, peri-urban, and rural areas, or even more varied environments (desert, forest, etc. ). Each image in VEDAI is available in both color and infrared versions. The spatial resolutions are 12. 5 cm for the 1024 × 1024 images and 25 cm for the 512 × 512 images, while object dimension varies between 16 and 40 pixels (for the 12. 5 cm version) and between eight and 20 pixels (for the 25 cm version). In our experiments, we exploit both 25-cm and 12.
5-cm versions (called VEDAI512 and VEDAI1024 in the sequel), and also both color and infrared versions to study the performance of our proposed model. Note that, for the 12. 5 cm version (VEDAI1024), the images were cut into patches of 512 × 512 pixels to fit the network's input size. The original dataset contains 3 757 objects belonging to nine different classes: car, truck, pickup, tractor, camper, ship, van, plane, other. However, since there are only few objects of the class "plane", we decided to merge them with the class "other". An illustrative image is shown in Figure  3. A precise experimental protocol by cross-validation is also provided by the authors of  [31] , ensuring that the experimental results obtained by different models can be properly replicated and compared. Indeed, the dataset that contains 1250 images is divided into training and validation sets using 10-fold cross-validation (it is divided into 10 subsets and at each experiment, nine are used for training, and the rest is used for validation).
MUNICH We also consider the DLR 3K Munich Vehicle dataset  [32]  (called MUNICH in this section). Similarly to VEDAI, MUNICH offers a large number of small vehicles and a large variability of backgrounds. The dataset contains 20 large images of size 5616 × 3744 pixels taken by a 1000 m-high RGB sensor with a spatial resolution of 13 cm. Two classes of vehicles are considered: 9300 cars and 160 trucks. MUNICH is particularly interesting to evaluate detectors in a context where the number of objects of different categories is largely unbalanced due to its high class imbalance property. In this work, we first cut the large images into patches of 1024 × 1024 pixels, then resize them into 512 × 512 pixels (which corresponds to a spatial resolution of 26 cm), so that the vehicles would have smaller sizes (from 8 to 20 pixels).
XVIEW The third and last dataset considered in our study is XVIEW  [33]. It was collected from WorldView-3 satellites at 30-cm spatial resolution (ground sample distance). A total number of 60 classes are available, but since we focus here on small objects, we gather 19 classes of vehicles, including {17, 18, 19, 20, 21, 23, 24, 26, 27, 28, 32, 41, 60, 62, 63, 64, 65, 66, 91} (these numbers correspond to the initial classes from the original XVIEW data) to create only one vehicle class. Our purpose is not to achieve state-of-the-art detection rate on the XVIEW dataset, but to experiment and validate the capacity of YOLO-fine to detect vehicles from such high resolution satellite images. This single-class dataset presents the highest number of training and validation images with a total of 35k vehicles whose size varies from six to 15 pixels.
Experimental Setup and Evaluation Criteria Experiments were conducted on our three datasets to study the performance of the proposed YOLO-fine as compared to several state-of-the-art object detection frameworks. We considered several detection models from the YOLO family (https://github. com/AlexeyAB/darknet), including YOLOv2  [11] , YOLOv3  [12] , YOLOv3-tiny (which is a fast and light version of YOLOv3) and YOLOv3-spp (which is YOLOv3 with spatial pyramid pooling operator  [56] ). We also investigated other one-stage and two-stage methods, such as SSD (https://github. com/lufficc/SSD) [13, 57] , Faster R-CNN (https://github. com/facebookresearch/maskrcnn-benchmark) [6, 58]  (both with VGG16 backbone), RetinaNet (https://github. com/yhenon/pytorch-retinanet) [15]  (with ResNet-50 backbone), and EfficientDet (https://github.
com/zylo117/Yet-Another-EfficientDet-Pytorch) [18]  to enrich our comparative study. Although different implementations were adopted (mentioned in the provided links), equivalent parameters were set in order to perform a fair comparison. We retained the standard parameter setting of each framework and adapted the anchor sizes with regard to the training set of each dataset. The input size to all networks was fixed to 512 × 512 and batch size was set to 64. All of the detectors were trained with a learning rate of 0. 001 with a momentum of 0. 9 during a fixed number of 200 epochs from which the best detection result was reported for each detector. The following standard criteria are exploited to quantitatively evaluate and compare the detection accuracy:.
Detection Performance The detection performance is now assessed on each datasets (VEDAI, MUNICH, and XVIEW). Note that for VEDAI, we focus on VEDAI512 dataset (with 25-cm resolution) as it is more relevant to our paper scope (objects size is smaller (from eight to 20 pixels)). Nevertheless, detection results on VEDAI1024 dataset are provided in order to show that YOLO-fine provides slightly better or at least equivalent accuracy for detecting medium and larger objects. Finally, the computational time of YOLO-fine compared to other YOLO versions are reported, performed on three different GPUs.
From Table  4 , similar results were obtained on the infrared VEDAI512. Let us note that this specific VEDAI infrared version has rarely been investigated in the literature. We are interested here to assess the performance of our model on infrared images, which are of high importance in many remote sensing scenarios. Our first observation is that, for this dataset, only exploiting the infrared band also provided equivalent results to the use of color bands. This remark shows that infrared VEDAI images also contain rich information for discriminating different vehicle classes, as good as the three color bands. Back to the detection results from Table  4a , YOLO-fine achieved the highest mAP of 68. 83%, i. e. , a gain of 5. 27% when compared to the second-best result of YOLOv3-spp (mAP = 63.
VEDAI1024 Color and Infrared Table  5  provides the detection results on VEDAI1024 (12. 5-cm resolution). This dataset is a high-resolution version of the previous one which was also proposed by the authors in  [31]. We remind the size of the vehicles in VEDAI1024 ranges from 16 to 40 pixels, thus double the size of those in the previous VEDAI512. We consider the objects in VEDAI1024 are of medium size and not small size. By conducting experiments on this dataset, we show that, although YOLO-fine is designed to mainly detect small objects, it could be able to provide at least equivalent detection accuracy as the original YOLOv3 for other object sizes. Let us observe Table  5 , which shows the comparative detection results of different detectors yielded on the color and infrared versions of VEDAI1024.
Globally, the detection results of all detectors are better than those on VEDAI512 version. In particular, we now observe good results from reference detectors, including SSD, Faster R-CNN, RetinaNet, and, in particular, EfficientDet, which was not the case for VEDAI512, as previously reported. This could be explained by the fact that objects in VEDAI1024 are larger so that these state-of-the-art detectors could naturally provide their good detection capacity. For our YOLO-fine model, while providing significant improvement w. r. t. the previous VEDAI512 dataset (i. e. , gain of 6. 09% and 5. 07% in mAP on the color and infrared versions, respectively), it now provides slightly better performance than other reference models on VEDAI1024. For the color version (left part of Table  5 ), it achieved a mAP = 76.
MUNICH Table  6  provides the detection results of YOLO-fine compared to reference detectors for the MUNICH dataset. A very high detection rate was achieved by our YOLO-fine (mAP = 99. 69%) with an increase of 1. 82% over YOLOv3 (second-best), 2. 12% over SDD (third-best) and 19. 93% over YOLOv2 (lowest mAP). In terms of F1-score, both YOLO-fine and SSD provided the highest value of 0. 98. Compared to VEDAI512 (with similar spatial resolution and object size), MUNICH appears less complicated, because: 1/ it contains only two very different classes, namely "car" and "truck" classes (as compared to the eight classes of vehicles that look similar in VEDAI images); 2/ the number of objects in MUNICH is higher (9460 compared to 3757 in VEDAI); and, 3/ the level of background variation is low (the images were all acquired from a semi-urban area of the city of Munich).
XVIEW The last dataset used in our experiments is XVIEW and the detection results measured on this dataset are given in Table  7. Some illustrative results are also given in Figure  10  in order to show a good quality of detection results yielded by YOLO-fine, close to the ground truth boxes. As observed with the two previous datasets, YOLO-fine again provided best detection mAP = 84. 34%, with a gain of 1. 83% over EfficientDet, 5. 41% over YOLOv3, and even 16. 25% over SDD and 27. 33% over Faster R-CNN. We note that objects in XVIEW are quite small when compared to those in MUNICH or VEDAI (spatial resolution of 30 cm compared to 25 cm in MUNICH and VEDAI512). Thus, the mAP = 84. 34% achieved by YOLO-fine could be considered as successful for real-time prediction using a one-stage approach.
One may wonder why this result is higher that those of VEDAI512. The answer is that for XVIEW, we only consider one class of vehicle (which was, in fact, merged by 19 vehicle classes from the original dataset as described in Section 4. 1), hence the detection task becomes simpler than in the VEDAI dataset. To this end, the proposed YOLO-fine is thus able to perform small object detection from both aerial and satellite remote sensing data. Ground truth Finally, using the XVIEW data, we conducted a comparative study in terms of computational performance. We note that only detector models from the YOLO-family were investigated, since the detectors were implemented using different frameworks, hence hindering a fair comparison related to computational time. In Table  8 , we report the comparison of YOLO-fine against YOLOv2, YOLOv3, YOLOv3-tiny, and YOLOv3-spp in terms of network weights (i.
One may wonder why YOLO-fine, with several removed layers and significantly lighter weight than YOLOv3, is still slower (34 FPS compared to 37 FPS on Titan X). This is due to the fact that we refine the detection grid to look for small and very small objects. However, this slower speed is not significant w. r. t. the great gain in accuracy observed against YOLOv3 shown in previous results with our three datasets. We can also note that YOLOv2 remains the fastest detector among the YOLO family (as observed in the literature) with 57 FPS on Titan X, 84 FPS on RTX 2080i, and 97 FPS on Tesla V100. Subsequently, the YOLOv3-tiny is a very light version of YOLOv3 with small weight size (34. 7 MB compared to 246. 4 MB), only 8. 25 BFLOPS when compared to 98. 92 BFLOPS and very fast prediction time (215 FPS on Tesla V100).
YOLO-fine For these experiments, we relied on the 25-cm VEDAI512 dataset by dividing it into three subsets, each corresponding to a different type of background. Indeed, we split the data into a training set composed of images acquired in rural, forest and desert environments, and two validation sets with new backgrounds representing residential areas on the one hand, and very dense urban areas on the other hand. Figure  11  illustrates some sample images from the training set and the 2 validation sets (namely "urban" and "dense urban"). Table  9  provides some information related to the number of images and objects of each class included in the three sets. Even if VEDAI offers a limited number of images and objects, we make sure each class comes with enough samples in the training set, but also in the two validation sets in order to conduct a fair comparative study.
Conclusions and Future Works In this paper, we have presented an enhanced one-stage detection model, named YOLO-fine, to deal with small and very small objects from remote sensing images. Our detector was designed based on the state-of-the-art YOLOv3 with the main purpose of increasing the detection accuracy for small objects while being light and fast to enable real-time prediction within further operational contexts. Our experiments on three public benchmark datasets, namely VEDAI, MUNICH, and XVIEW, demonstrate the overall superiority of our YOLO-fine model over the well-established YOLOv3 solution. Indeed, YOLO-fine provides the best compromise between detection accuracy (highest mAP), network size (smallest weight size), and prediction time (able to perform real-time prediction). While YOLO-fine brings promising results, some issues still remain and call for further research.
First, as already mentioned, YOLO-fine focuses on small and very small objects, while its performances on medium and large objects remains comparable to YOLOv3, but with a slower prediction speed. In other words, YOLO-fine might not be the best choice if the sought objects have a very wide range of sizes. This bottleneck could be solved by making a transparent pass between YOLO-fine and YOLOv3 in the same model, allowing for an efficient detection of objects from very small sizes to very large sizes. Second, our investigation related to the behavior of YOLO-fine when dealing with new background appearances has provided some preliminary good results, but it remains limited to the proposed split made on the VEDAI dataset. It would be interesting to explore further this issue and consider other datasets where the difference between backgrounds in training and test scenes could be more significant.
Datasets VEDAI1024 VEDAI512 MUNICH XVIEW Number of images 1200 1200 1566 7. 4 k in the train set 1089 1089 1226 5. 5 k in the validation set 121 121 340 1. 9 k Number of classes 8 8 2 1 Number of objects 3757 3757 9460 35 k Spatial resolution 12. 5 cm 25 cm 26 cm 30 cm Object size (pixels) 16 → 40 8 → 20 8 → 20 6 → 15.
Considering a monitoring task for soil sampling in a forest, they discuss both low-level hardware issues and high-level communication/coordination issues. A particular threat for real-world robot swarms is a possible attack by malicious agents that could be introduced into the swarm. The paper by Strobel et al. makes a significant contribution toward the use of swarm robotics in the real world by presenting a framework for a secure decentralized database. The presented framework uses smart contracts, a way to decentrally execute programs based on an Ethereum blockchain. Individual malicious robots aim to disrupt the collective decision-making process of a simulated swarm of epuck robots by spreading misinformation. The robot swarm successfully disregards the wrong information. The authors indicate that blockchain networks can be used for robot swarms, and the low processing and memory capacity of swarm robots does not prohibit the use of blockchains in real-world scenarios.
Contrary to the adage "there is safety in numbers, " robustness is not an inherent benefit of robot swarms that results from redundancy. Robustness is a challenging design goal, made complex by the interplay between the benefits of redundancy and the need for scalability. Wilson et al. argue that achieving robustness through redundancy involves a careful codesign of hardware, fabrication processes, and control software. To investigate this idea, the authors present an approach to achieving robustness that involves a novel hardware-software co-design of a modular robotic platform called "DONUts" (Deformable Self-Organizing Nomadic Units). The modules are inexpensive, flexible printed circuit boards, and designed to move as a collective through magnetic interaction. Wilson et al. study several control strategies that explore the design space of intermodule connectivity to shed light on the interplay between robustness, scalability, and controllability.
Rivers support some of Earth's richest biodiversity 1 and provide essential ecosystem services to society 2 , but they are often impacted by barriers to freeflow 3. In Europe, attempts to quantify river connectivity have been hampered by the absence of a harmonised barrier database. Here we show that there are at least 1. 2 million instream barriers in 36 European countries (mean density = 0. 74 barriers/km), 68% of which are low-head (<2 m) structures that are typically unreported. Standardised walkover surveys along 2,715 km of stream length in 147 rivers indicate that existing records underestimate barrier numbers by ~61%. The highest barrier densities occur in the heavily modified rivers of Central Europe, and the lowest in the most remote, sparsely populated alpine areas. Across Europe, the main predictors of barrier density are agricultural pressure, density of river-road crossings, extent of surface water, and elevation.
A critical challenge for quantifying river fragmentation is the lack of information on the abundance and location of all but the largest of dams, especially over spatial scales relevant for river basin management. Global database initiatives and novel developments in remote sensing are making it possible to accurately map the location of large dams, typically those above 10 m to 15 m high 3,10-12 , but these only represent a small fraction of all instream barriers, typically <1%  13. Most low-head structures are unreported  14  , despite the fact that their cumulative impact on river connectivity is far more substantial  15, 16. For instance, while only large storage dams can affect the hydrological regime  17  , nearly all barriers can affect sediment transport  18, 19  , the movement of aquatic organisms  20  , and the structure of river communities  15, 21.
Yet, there is no comprehensive inventory of stream barriers in Europe, only disparate records that differ in quality and spatial coverage from country to country  29, 30. Many weirs in Europe, for instance, were built at the turn of the 18 th century and sometimes much earlier, and their number and location are consequently poorly known  31, 32. Here we present the first comprehensive estimate of river fragmentation in Europe based on empirical and modelled barrier densities. We collated and harmonised 120 regional, national and global barrier datasets, and applied robust exclusion rules to identify unique barrier records. To account for underreporting, we surveyed 147 rivers in 26 countries to derive field-corrected barrier densities, and employed random forest regression (a machine learning technique) to estimate the number and location of missing barriers (Extended Data Fig.
Accounting for barrier underreporting Barrier inventories in Europe are not homogeneous with respect to barrier types, reach, or completeness (Table  1 ), as they were compiled for different purposes using different resources. They have different spatial coverage and suffer from strong sampling bias (Fig. 2a, b ) that result in under-reporting of small structures. We adopted two complementary strategies to account for barrier under-reporting and derive more realistic barrier densities (Extended Data Fig. 1 ): ground-truthing of existing barrier records via walkover field surveys in matched river reaches (a bottom-up strategy; Fig. 2b ; Extended Data Fig. 3 ), and barrier modelling at sub-catchment level using random forest regression (a top-down strategy; Fig. 2c ). Our study indicates that there are more barriers than existing databases would suggest.
Loss of connectivity depends mostly on the number and location of barriers, not on their height  46. As many of these barriers are small, old and obsolete, they provide unprecedented opportunities for restoring connectivity, which our study can help inform. Firstly, to restore connectivity efficiently, we call for better mapping and monitoring of barriers, particularly small ones, as they are the most abundant and the main cause of fragmentation. A concerted global effort is required to map low-head structures and complement existing dam databases. Although barrier density is only a crude measure of fragmentation, the number and location of barriers serves as the basis for most metrics of river connectivity  46. In this sense, our work highlights the merits, but also the limitations, of modelling fragmentation, and suggests that there is no substitute for a 'boots on the ground' approach for estimating barrier numbers and location  23, 34.
It also exposes the inadequacies of current barrier inventories, and emphasizes the need for complete, harmonized barrier databases in order to select the river catchments that offer the best prospects for restoration of connectivity. With nearly 630,000 records, the AMBER Barrier Atlas represents the most comprehensive barrier inventory available anywhere, but is far from being complete. A staggering 0. 6M barriers are probably missing from current inventories. Importantly, our study can help optimise future mapping efforts, and fill data gaps where information is lacking. For example, our field surveys indicate that existing records grossly underestimate the abundance of small barriers (Log Likelihood Ratio = 97. 94, df = 5, P < 0. 001; Fig. 3a ), particularly fords, culverts and sluice gates (LRT = 44.