A lightweight dead fish detection method based on deformable convolution and YOLOV4

Abstract

The proposal of deep neural network achieves intelligent detection of abnormal fish behaviors. However, with the increase of network depth, the defects of large training memory and poor real-time performance restrict the deployment of the algorithm in aquaculture end devices. Therefore, this paper proposes a high-precision and lightweight end-to-end target detection model based on deformable convolution and improved YOLOv4. First of all, replacing the YOLOv4 backbone network with the lightweight network MobileNetV3 and replacing the standard convolution with a deep separable convolution have achieved a significant reduction in network parameters and calculations; Secondly, deformable convolution is used to improve the target feature extraction ability and increase the detection accuracy of the model in underwater images; Finally, an ablation experiment is conducted to compare the detection effect under different deformable convolution layers and network positions. Experimental results show that the combination of three-layer deformable convolution and standard convolution has the best performance. Compared with the YOLO series, the proposed model has an accuracy of 95.47% while the parameter amount is reduced by 10 times and the FPS is doubled. Rapid detection of dead fish is achieved in real circulating aquaculture systems.