Ship crack detection based on lightweight fast convolution and bidirectional weighted feature fusion network

  Objectives  Traditional ship crack detection methods based on artificial visual inspection and ultrasonic methods in ship repair and inspection processes have the characteristics of low efficiency, high cost, and high danger, a ship crack detection method based on deep learning is proposed.

  Methods  First, a lightweight convolutional structure (GSConv) is used to replace the standard convolution and introduce attention mechanism in the backbone of YOLOv5s to achieve the reduction of network parameters and computation while enhancing the ability to extract crack features. Secondly, C3_Faster constructed by fast convolutional structure is used instead of the original C3 module in the neck of the network to improve the processing speed of the model and enhance its rapidity. Finally, designed a simplified bidirectional weighted feature fusion network (BiFFN) to enhance eeature aggregation in the original model (YOLOv5s) for Improved fusion of semantic and spatial information of cracks, and enhanced accuracy and localization precision in crack recognition.

  Results  By training on both original and augmented ship crack datasets, the proposed method achieves a detection accuracy of over 94.11% and a recall rate of over 93.50%, while reducing the computational complexity by 17.93% and the parameter count by 15.81%.

  Conclusion  The study demonstrates that the ship crack detection based on lightweight fast concolution and bidirectional weighted feature fusion network(MLF-YOLO), achieves lightweight model architecture and high detection accuracy and recall rates. This provides a reference for the development of UAV− ship autonomous inspection systems.