# Developing a Dead Fish Recognition Model Based on an Improved YOLOv5s Model
## 1 Introduction
This was achieved by using YOLOv5s in combination with SiamRPN++ to detect small targets in complex scenes. However, the method has room for improvement, and subsequent consideration should be given to the study of multi-target tracking. Meanwhile, the dataset contains only one kind of abnormal behavior, and it is necessary to expand the dataset. [PERSON] et al. [19] used the CLAHE image enhancement algorithm and introduced PConv in the YOLOv7 backbone network to improve the accuracy of the network model for dead fish detection. [PERSON] et al. [20] proposed an enhanced YOLOv5s model to address the challenges posed by occlusion, motion blur, and the detection of small targets. The model incorporates an attention mechanism, a BiFPN feature fusion approach, and a lightweight upsampling operator called Carafe. However, this experiment was also hindered by an inadequate dataset. [PERSON] et al. [21] proposed an improved YOLO-S model for the real-time detection of abnormal behavior in the yellow coraker, which improved the performance of the model by increasing coordinate attention. However, this method increases the parameters and computation of the model, which needs to be further developed to meet real-time requirements. In the area of dead fish identification on the water surface, due to the requirements of dataset collection and detection tasks, research often involves small object detection. Feature fusion is one of the strategies for enhancing small object detection through improved feature representation. [PERSON] et al. [22] used deeply and multi-scale fused millimeter-waveradar point cloud data and visual information for unmanned surface vehicles and used the characteristics of radar data to improve the accuracy and robustness of surface target detection. [PERSON] et al. [23] proposed a multi-feature fusion strategy using multi-scale and dilated convolution for fabric surface defects, incorporating a residual structure to capture multi-scale features and improve the model's detection rate. [PERSON] [24] proposed a local-global feature fusion method for the study of small target detection using drones, aimed at improving the efficiency of cross-layer feature fusion and effectively extracting the shallow features necessary for small object detection. [PERSON] et al. [25] proposed that the TMFD model can be used for fish detection in a variety of scenarios. The feature extraction capability was further strengthened by adding the MCB module, which improves the features of fish with different scales and postures, and the DBMFormer binary fusion attention mechanism. In subsequent work, the model needs to be studied for lightweighting. Currently, most detection algorithms for dead fish or floating debris on the water surface focus on small object detection and enhancing attention to target features. Feature fusion is mostly approached from the network structure or algorithmic level. However, using a single size convolution kernel has poor adaptability to dead fish at varying distances, often overlooking feature details of different scales, which significantly limits model performance. There are very few reliable and targeted feature fusion modules. This paper proposes a multi-scale convolution module, DWMConv, based on depthwise separable convolution. Additionally, the EMA mechanism is embedded in C3. Finally, while ensuring accuracy, the C3-Light module is used to reduce the parameter count and computation. Various comparative experiments have verified that this method can effectively improve the detection accuracy of dead fish.