## 2 Data and Method
### Method
#### 2.2.2 Development and Test of Computer Vision Models  
Detecting small and dense objects presents inherent challenges. Over time, numerous improvements have been introduced to object detection frameworks to enhance the accuracy of detecting [23]. In our research, we developed a computer vision model using YOLOv8 (Figure 3), which consisted of a backbone for feature extraction, a neck for combining multi-scale features, and a head for object detection. YOLOv8 improved detection performance with its efficient architecture, combining CSPNet and PANet for better feature propagation and localization accuracy. This allowed it to perform real-time detection while maintaining high precision across varying object scales. To address the challenge of detecting dense clusters of small fishing vessels, we employed two key optimization modifications: (1) We integrated multi-scale feature fusion to combine low-level feature maps, which provide detailed positional information, with high-level feature maps, rich in semantic context. This integration was key to improving the detection performance,particularly for small targets like dense clusters of fishing vessels (Figure 3, YOLO-P2). (2) We replaced strided convolution and/or pooling layers, which often lead to fine-grained information loss, with a CNN building block called SPD-Conv to create a more effective model [31] (Figure 3, YOLO-SPD).  
Figure 3: The overall framework of the original YOLOv8, YOLO-P2, and YOLO-SPD networks. In the YOLO-SPD model, SPD-Conv is integrated into the YOLOv8 backbone to enhance performance. Meanwhile, YOLO-P2 retains the YOLOv8 backbone but adds a P2 detection head to improve small target detection.  
Figure 2: Typical nighttime remote sensing images observed by VIIRS-DNB with squid jiggers: (**A**) Clearly identifiable and distinguishable lit fishing vessels. (**B**) Blurry but distinguishable and locatable lit fishing vessels. (**C**) Obscured lit fishing vessels, only locatable.  
Labeled images from the year 2020 were selected for comparative testing, while the remaining images were randomly allocated, with 90% used for training and 10% for validation. Due to the nature of dense small targets, the criterion for a true positive was adjusted to an intersection over union threshold of 10%. The training parameters were configured as follows: a batch size of 16, a total of 500 iterations, and an input image resolution of 640 \(\times\) 640 pixels. All other parameters were kept at their default settings. The experimental setup is outlined in Table 1.  
In the experiment, computer vision models were evaluated using several metrics, including precision, recall, accuracy, F1 score, mean average precision (mAP), frames per second (FPS), and giga floating point operations per second (GFLOPS). The formulas for these key metrics are as follows:  
\[\text{Precision }(\text{P}):\text{ P}=\frac{\text{TP}}{\text{TP+FP}}\] \[\text{Recall }(\text{R}):\text{ R}=\frac{\text{TP}}{\text{TP+FN}}\] \[\text{F1 Score }:\text{ F1}=2\ \times\frac{\text{P}\times\text{R}}{\text{P}+\text{R}}\]  
The definitions of the parameters in the formulas are as follows: true positive (TP) represents the samples correctly identified as positive, false positive (FP) represents the samples incorrectly identified as positive, false negative (FN) represents the samples incorrectly identified as negative, and true negative (TN) represents the samples correctly identified as negative. Additionally, mAP (mean average precision) measures the average area under the precision-recall curve for each target across all images, providing a comprehensive assessment of the precision and recall performance. FPS is the inverse of the total time required to process a single frame, encompassing both the inference time and the non-maximum suppression time. GFLOPS (giga floating point operations per second) represents the computational cost of performing the forward pass (inference) in billions of floating point operations per second.