Underwater Object Detection Method with Enhanced Wavelet Transform Features - Nan WEI; Wankou YANG; Weijie ZHOU; Longyu JIANG (水下无人系统学报, 2025)
The complex and unique underwater environment results in low-quality underwater images, characterized by low contrast, blurriness, and underwater degradation, which significantly affects the capabilities of underwater object detection. To address this issue, this paper proposed an underwater object detection method with enhanced wavelet transform features. The paper introduced discrete wavelet transform(DWT) to decompose the multi-level features extracted by the deep learning framework into high- and low-frequency components. These frequency domain feature components were then interactively enhanced using a frequency domain interaction module based on the attention mechanism designed in this work, optimizing the ability of feature expression. The enhanced features were subsequently fed into the object detection network to improve the object detection performance. Experimental results demonstrate that the proposed underwater object detection method outperforms conventional object detection methods, significantly improving the ability to detect objects in underwater environments.