Underwater object detection algorithm based on channel attention and feature fusion

Abstract

Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection. In order to solve the above-mentioned problems, an underwater object detection algorithm is proposed based on the channel attention and feature fusion for underwater optical images. The excitation residual module is designed based on the channel attention, and the forward propagation feature information is adaptively allocated weights to highlight the salience of different channel feature maps, which improves the network ability to extract high-frequency information from the underwater images. The multi-scale feature fusion module is designed to add a large scale feature map for object detection, which improves the detection performance of the network for small size objects by using its corresponding small size receptive field, and further improves the detection accuracy of the network for different size objects in the underwater environment. To improve the generalization performance of the network to the underwater environment, the data augmentation method based on the stitching and fusion is designed to simulate the overlap, occlusion and blurring of underwater objects, which improves the adaptability of the network to the underwater environment. Through experiments on the public dataset URPC, the algorithm in this paper improves the mean average precision by 5.42%, 3.20% and 0.9% compared with YOLOv3, YOLOv4 and YOLOv5, respectively, effectively improving the missed and false detection of objects of different sizes in complex underwater environments.

Keywords

• underwater image
• object detection
• channel attention
• multi-scale feature fusion
• deep learning