Underwater Target Detection Algorithm Based on Feature Fusion Enhancement

Underwater robots that use optical images for dynamic target detection often encounter image blurring, poor contrast, and indistinct target features. As a result, the underwater robots have poor detection performance with a high rate of missed detections. To overcome these issues, a feature-enhanced algorithm for underwater target detection has been proposed in this paper. Based on YOLOv7, a feature enhancement module utilizing a triple-attention mechanism is developed to improve the network's feature extraction ability without increasing the computational or algorithmic parameter quantity. Moreover, comprehensively considering the impact of a redundant feature in the images on detection accuracy, the ASPPCSPC structure was built. A parallel spatial convolutional pooling structure based on the original feature pyramid fusion structure, SPPCSPC, is introduced. The GhostNet network was utilized to optimize its convolution module, which reduces the model's parameter quantity and optimizes the feature map. Furthermore, a Cat-BiFPN structure was designed to address the problem of fine-grained information loss in YOLOv7 feature fusion by adopting a weighted nonlinear fusion strategy to enhance the algorithm's adaptability. Using the UPRC offshore dataset for validation, the algorithm's detection accuracy was increased by 2.9%, and the recall rate was improved by 2.3% compared to the original YOLOv7 algorithm. In addition, the model quantity is reduced by 11.2%, and the model size is compressed by 10.9%. The experimental results significantly establish the validity of the proposed algorithm.