Lightweight Object Detection Model for Underwater Sonar Images

Abstract

Abstract: With the development of unmanned underwater detection technology， AUV with sonar detection has become the main method of underwater object detection. However， due to the complexity of the underwater environment and the limitation of the sonar imaging mode， the sonar image resolution is low. Therefore， the traditional morphology based on object detection method has the problems of low detection accuracy and poor real-time performance. When deep learning algorithms such as YOLO are directly applied to underwater sonar image target detection， they still face challenges such as few underwater samples and many model parameters. This paper proposes a lightweight object detection model for sonar image datasets. In view of the characteristics of low-resolution sonar image data and the real-time requirements of underwater AUV automatic detection， the YOLOv4 model is used as the main framework to carry out model tailoring， replace the optimized feature fusion module， target prediction K-means clustering and improve the loss function， etc.， and the constructed detection model is applied to sonar target detection. According to the experimental data， the mAP of the proposed model in this paper is 0.0659，0.0214，0.0402 and 0.1701 higher than that of SSD， YOLOv3， YOLOV3-DFPIN and YOLOV4-tiny respectively， Under the conditioms of the mAPs are only 0.0186 lower than that of YOLOv4， only 0.0093 lower than CenterNet， only 0.0074 lower than EfficientdetD0， however， FPS is more than twice as high as YOLOv4 and CenterNet， more than fifth as high as EfficientdetD0. At the same time， the proposed model in this paper has the advantages of both high precision and real time. The experimental results show that the proposed feature extraction network can greatly reduce the redundancy of network parameters and improve the model efficiency and detection speed. Combined with the adaptive spatial feature fusion module， the mutual fusion and reuse of features in different scales are enhanced， and the accuracy of low resolution sonar image target detection is improved.

Key words: object detection, underwater sonar image, deep learning, YOLOv4, Kmeans++

FAN Xin-nan, CHEN Xin-yang, SHI Peng-fei, SUN Huan-ru, LU Liang, ZHOU Zhong-kai. Lightweight Object Detection Model for Underwater Sonar Images[J]. Computer and Modernization, 2023, 0(03): 16-22.

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