Research on Ship Detection Algorithm Based on Deep Learning
With the development of the marine transportation industry,offshore safety issues have increasingly become prominent,highlighting the significance of automated detection for surface vessels.This article investigates a ship detection method based on deep learning.By enhancing the YOLOv7-tiny model,the CBAM(Convolutional Block Attention Module)attention mechanism is introduced into the network,along with improvements in the structure of the detection head network,thereby increasing the model's accuracy for ship detection.Experimental results demonstrate that the adoption of the improved model achieves a ship detection and recognition accuracy rate of 98.7%,enabling accurate and efficient automated detection of offshore vessels.
deep learningoffshore safetyYOLOv7-tinyship detection
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维普
