Mobilenetv3-YOLOv4-Sonar: Object Detection Model Based on Lightweight Network for Forward-Looking Sonar Image

Abstract

Real-time performance and accuracy are the standards to measure the detection algorithm performance. The existing detection methods for forward-looking sonar target image are difficult to achieve ideal balance between the two standards. Due to the influence of background interference and non-rigid deformation, it hardly obtains the high detection accuracy in fast detection. In this paper, we propose a Mobilenetv3-YOLOv4-Sonar algorithm, which is a more accurate and lightweight target detection model. The backbone of YOLOv4 adopts Mobilenetv3, which is improved by Convolutional Block Attention Module (CBMA) and modified Squeeze-and-Excitation Network (SENet). Consequently, we can eliminate the influence of high-light background interference and reduce the complexity of the model. Meanwhile, the improved SPPnet is used as the neck of YOLOv4 to reduce the failure of target detection caused by non-rigid deformation. Extensive experiments and comparisons with state-of-arts detectors illustrate that the accuracy of our model has increased by 4.6% compared with YOLOv4 on the dataset, and the model parameters are only one tenth of the traditional YOLOv4, which has practical significance for target detection in forward-looking sonar images.