LMFFNet: Lightweight Multiscale Feature Fusion Network for Underwater Structural Defect Detection

ABSTRACT Complex physicochemical environmental effects result in highly intricate and diverse backgrounds in underwater object images, posing significant challenges for detecting structural defects. Besides, current methods overlook the tradeoff between the detection accuracy and computational cost. To effectively address the aforementioned issues, we present a lightweight multiscale feature fusion network (LMFFNet) for underwater structural defect detection. Aiming to enhance the feature representability, spatial attention, and bidirectional pyramid modules are jointly employed for fusing multiscale features. A parameter‐sharing header module is designed to reduce the model parameters. The dynamic nonmonotonic focusing mechanism is introduced in the loss function, which can improve the defect detection performance on degraded underwater images. Comprehensive experiments on a real‐world underwater data set demonstrate the superiority of the LMFFNet over existing state‐of‐the‐art methods.