YOLOv7 F: A lightweight real time ship detection algorithm

DOI:	10.11809/bqzbgcxb2024.11.002
Keywords:	ship detection; deep learning; lightweight model; attention mechanism;YOLOv7
Abstract:	Ship detection is crucial in inland waterway transportation management, and it is challenging to balance accuracy and real time performance in complex water surface conditions.To address these issues in ship real time detection, the YOLOv7 F model based on an improved YOLOv7 model has been proposed.GhostNet is introduced into the backbone network for feature extraction, and the distributed shift convolution is introduced into the feature fusion network to achieve model lightweight.An attention mechanism is introduced into the feature fusion network to compensate for the accuracy loss caused by model lightweight.The loss function is improved to make the detection model more suitable for ship datasets.Experimental results on the HPRship dataset show that compared with the traditional YOLOv7 detection model, the computational cost is reduced by 38.8×109, the model parameter quantity is reduced by 5.7×106, and the detection accuracy mAP0.5 is increased by 0.7% to 98.80%.YOLOv7 F achieves a good balance between lightweight and detection accuracy, allowing for accurate real time ship detection tasks and is suitable for deployment on small devices with limited storage and computing resources.
Published:	2024-11-30
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