EPL-UFLSID: Efficient Pseudo Labels-Driven Underwater Forward-Looking Sonar Images Object Detection

Cheng Shen, Liquan Shen, Mengyao Li, Meng Yu

Abstract: Sonar imaging is widely utilized in submarine and underwater detection missions. However, due to the complex underwater environment, sonar images suffer from complex distortions and noises, making detection models hard to extract clean high-level features for detection. Existing works introduce denoised images as pseudo labels to assist the network to extract clean features while not fully considering the rationality of pseudo labels. To this end, we propose an Efficient Pseudo Labels-Driven Underwater Forward-looking Sonar Images Object Detection algorithm (EPL-UFLSID). Specifically, we first design a Gaussian Mixture Model based Deep Image Prior (GMMDIP) network to generate denoised sonar images by setting the GMM distribution as its input. After that, to filter the most detection-friendly images of the denoised images generated by GMMDIP as efficient pseudo labels, Detection-Friendly Image Quality Assessment network (DFIQA), is designed, which is also able to help EPL-UFLSID further distill cleaner features from pseudo labels to improve detection performance. Extensive experimental results show that our EPL-UFLSID reaches average precision (AP) of 67.8\%/39.8\% and average recall (AR) of 73.7\%/49.6\% on two real sonar datasets, which outperforms SOTA underwater forward-looking sonar images object detection algorithms.

Primary Subject Area: [Content] Vision and Language

Secondary Subject Area: [Generation] Generative Multimedia

Relevance To Conference: Underwater forward-looking sonar can detect the underwater world, but the video or pictures it acquires are difficult to recognize by the human eye. This work could help people detect target objects more accurately with underwater forward-looking sonar.

Submission Number: 2823