Study on improved YOLOv5s-based ship water gauge detection model
Ship water gauge detection is a key in image-based detection technology of ship draft depth.During detection,it faces challenges such as gauge offset,rotation,distortion,and small target sizes,making accurate identification detection difficult.In this paper,an improved YOLOv5s ship water gauge detection model was proposed for enhancing feature extraction capabilities for irregular and distorted targets through the incorporation of Deformable Convolutional Networks(DCN).Additionally,the model incorporated the Convolutional Block Attention Module(CBAM)to improve feature representation in key areas of the target.A ship water gauge dataset was developed based on on-site monitoring images from the Gaogang Ship Lock in Taizhou for testing purposes.Results demonstrate that the improved model shows a significant improvement in terms of precision,recall,Fl score,mAP@0.5,and mAP@0.5:0.95 compared to the original model.Moreover,the overall performance of this model is superior to that of commonly used mainstream algorithms,effectively improving the accuracy of ship water gauge detection.It provides an important support for the automatic detection of ship draft depth.
ship water gauge detectionYOLOv5s modeldeformable convolutionattention mechanismdeep learning
万方数据
维普
