Research of Maritime Object Detection Method in Foggy Environment Based on Improved Model SRC-YOLO

An improved maritime object detection algorithm, SRC-YOLO, based on the YOLOv4-tiny, is proposed in the foggy environment to address the issues of false detection, missed detection, and low detection accuracy in complicated situations. To confirm the model's validity, an ocean dataset containing various concentrations of haze, target angles, and sizes was produced for the research. Firstly, the Single Scale Retinex (SSR) algorithm was applied to preprocess the dataset to reduce the interference of the complex scenes on the ocean. Secondly, in order to increase the model's receptive field, we employed a modified Receptive Field Block (RFB) module in place of the standard convolution in the Neck part of the model. Finally, the Convolutional Block Attention Module (CBAM), which integrates channel and spatial information, was introduced to raise detection performance by expanding the network model's attention to the context information in the feature map and the object location points. The experimental results demonstrate that the improved SRC-YOLO model effectively detects marine targets in foggy scenes by increasing the mean Average Precision (mAP) of detection results from 79.56% to 86.15%.

Automated summary

An improved maritime object detection algorithm, SRC-YOLO, is proposed to address the issues of false detection, missed detection, and low detection accuracy in foggy environments. The algorithm applies a preprocessing algorithm, a modified module, and an attention module to improve detection performance. Experimental results show that the improved SRC-YOLO effectively detects marine targets in foggy scenes.