Real-time detection of underwater river crab based on multi-scale pyramid fusion image enhancement and MobileCenterNet model

Abstract

To achieve rapid and accurate detection of pond cultured river crab, an underwater river crab target detection method based on multi-scale pyramid fusion image enhancement and the MobileCenterNet model is proposed in this paper. Firstly, considering the characteristics of underwater crab image, such as blurred and uneven brightness, multi-scale pyramid fusion is proposed to enhance underwater crab image based on Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance contrast and Underwater Dark Channel Prior (UDCP) for fog removal. Secondly, a crab target detection method based on the MobileCenterNet model is presented. The improved MobileNetv2 backbone network with coordinate attention module is applied to extract crab features, which not only achieves lightweight but also focuses the model's attention on crab-related features. Then, the Feature Fusion Module (FFM) is designed to extract multi-scale feature map information, and Atrous Spatial Pyramid Pooling (ASPP) is added to fuse context information from different receptive fields. The experimental results show that the average precision (AP) and F1 values of MobileCenterNet are 97.86 % and 97.94 %, the model size is only 24.46 M, and the detection speed is reaching 48.18 frames/s. Compared with the baseline model ResNet18-CenterNet, the storage memory required for model training is reduced by 81 %, and the AP on the crab dataset is increased by 3.2 %. The experimental results show that the proposed method can achieve real-time and accurate detection of underwater river crabs, and provide effective guidance for real-time monitoring and scientific feeding in crab breeding.