Underwater Object Detection Method Based on Improved Faster RCNN

In order to better utilize and protect marine organisms, reliable underwater object detection methods need to be developed. Due to various influencing factors from complex and changeable underwater environments, the underwater object detection is full of challenges. Therefore, this paper improves a two-stage algorithm of Faster RCNN (Regions with Convolutional Neural Network Feature) to detect holothurian, echinus, scallop, starfish and waterweeds. The improved algorithm has better performance in underwater object detection. Firstly, we improved the backbone network of the Faster RCNN, replacing the VGG16 (Visual Geometry Group Network 16) structure in the original feature extraction module with the Res2Net101 network to enhance the expressive ability of the receptive field of each network layer. Secondly, the OHEM (Online Hard Example Mining) algorithm is introduced to solve the imbalance problem of positive and negative samples of the bounding box. Thirdly, GIOU (Generalized Intersection Over Union) and Soft-NMS (Soft Non-Maximum Suppression) are used to optimize the regression mechanism of the bounding box. Finally, the improved Faster RCNN model is trained using a multi-scale training strategy to enhance the robustness of the model. Through ablation experiments based on the improved Faster RCNN model, each improved part is disassembled and then the experiments are carried out one by one, which can be known from the experimental results that, based on the improved Faster RCNN model, mAP@0.5 reaches 71.7%, which is 3.3% higher than the original Faster RCNN model, and the average accuracy reaches 43%, and the F1-score reaches 55.3%, a 2.5% improvement over the original Faster RCNN model, which shows that the proposed method in this paper is effective in underwater object detection.