# Developing a Dead Fish Recognition Model Based on an Improved YOLOv5s Model
## 1 Introduction  
Intensive aquaculture is an important branch of fish farming. Influenced by factors such as temperature, water quality, and stocking density, the intensive farming of grass carp ('dead fish' in this paper refers specifically to grass carp) is associated with a high mortality rate due to diseases. To better and more quickly identify dead grass carp and reduce labor costs, the use of computer vision technology to achieve high-quality aqua-culture has become a new research focus. Recently, object detection has continuously developed in the field of computer vision and has been widely applied in areas such as autonomous navigation [1], facial recognition [2, 3], pedestrian detection [4, 5], aquaculture [6, 7], and fruit detection [8, 9]. In the field of dead fish identification, [PERSON] et al. [10] proposed an improved algorithm based on YOLOv4-v1 for identifying diseased and dead golden pomfret, achieving an average precision value of 98.31% in the identification of diseased and dead golden pomfret. This algorithm performs well in flat and complex environments, but its detection speed and leakage rate could be improved compared to the more advanced YOLOv5 algorithm. [PERSON] et al. [11]proposed a method for detecting dead fish called FFA-SSD (SSD with feature fusion and attention), which is based on single-shot multi-box detection, achieving a detection accuracy of 93.5%. FFA-SSD has better feature extraction capabilities and fewer parameters than VGG16 and ResNet50, but its superiority and usability are questionable when compared to the mainstream YOLO algorithms. [PERSON] et al. [12] combined deformable convolution and the SE attention mechanism to improve the YOLOv4 model, which improved the detection accuracy of dead fish. This algorithm is lightweight, fast, and implantable and can be good for the accurate real-time detection of dead fish but is limited by the dataset, which can lead to the false detection of normal swimming and turning of the fish. [PERSON] et al. [13] proposed a dead fry detection model based on E-YOLOv4 and D-YOLOv4 and established a dead fish detection platform with practical application value. This method improves the performance of the YOLOv4 model, reduces the size of the model, can be subjected to high levels of water surface interference, and takes into account additional artificial light to reduce the effects of water surface reflections. [PERSON] et al. [14] improved the YOLOv8 model to achieve the real-time detection of dead fish in aquaculture using drones combined with artificial intelligence, yielding excellent results. Improvements to the model have improved the identification of small targets, and the use of drones for surveillance is an efficient and accurate method, but drones are unable to fly missions in harsh environments, and rain can result in blurred images that cannot be accurately identified. In a study by [PERSON] et al. [15] aiming at the problems of feature blurring, small targets, and fish movement in dead fish detection, a single-center detection model, DF-DETR, is proposed to achieve high-precision real-time dead fish detection; however, different aquaculture environments and lightweighting are the issues to be further optimized in this model. [PERSON] et al. [16] proposed a dead fish detection model named DD-IYOLOv8. By adding a probe and mixing attention mechanism, the model's perception of dead fish and the accuracy of the model were improved. Detecting dense objects is a follow-up study that needs to be conducted for this model. [PERSON] et al. [17] et al. proposed a probe head capable of integrating the multi-scale features DCNv4-Dyhead and EMA-SlideLoss loss function combined with YOLOv9 to achieve the detection of abnormal fish behavior and counting in complex aquatic environments. The experimental datasets were derived from laboratory simulations, not real aquaculture complex environments. [PERSON] et al. [18] proposed a method capable of detecting and tracking fish exhibiting rollover behavior.