YOLOv7-CSAW for maritime target detection
نویسندگان
چکیده
Introduction The issue of low detection rates and high false negative in maritime search rescue operations has been a critical problem current target algorithms. This is mainly due to the complex environment small size most targets. These challenges affect algorithms' robustness generalization. Methods We proposed YOLOv7-CSAW, an improved algorithm based on YOLOv7. used K-means++ for optimal determination prior anchor boxes, ensuring accurate match with actual objects. C2f module was incorporated lightweight model capable obtaining richer gradient flow information. model's perception features increased non-parameter simple attention (SimAM). further upgraded feature fusion network adaptive (ASFF) address lack high-level semantic Lastly, we implemented wise intersection over union (WIoU) loss function tackle large positioning errors missed detections. Results Our extensively tested dataset YOLOv7 as baseline model. observed significant improvement performance compared traditional deep learning algorithms, mean average precision (mAP) 10.73% Discussion YOLOv7-CSAW significantly enhances accuracy scenes. effectively addresses common issues experienced operations, specifically improving reducing negatives, proving be superior alternative
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ژورنال
عنوان ژورنال: Frontiers in Neurorobotics
سال: 2023
ISSN: ['1662-5218']
DOI: https://doi.org/10.3389/fnbot.2023.1210470