Improvement of ablation experiment results for YOLOv7 modules.

Improvement of ablation experiment results for YOLOv7 modules.

<p>(a) The original YOLO model (b)YOLOv7+SimAM. (c)YOLOv7+SimAM+Pconv.</p>

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Bibliographic Details
Main Author:	Guoqing Zhang (151441) (author)
Other Authors:	Jiandong Liu (6064643) (author), Yongxiang Zhao (671364) (author), Wei Luo (80175) (author), Keyu Mei (20542959) (author), Penggang Wang (6038210) (author), Yubin Song (3205194) (author), Xiaoliang Li (720274) (author)
Published:	2025
Subjects:	Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicles uavs ), utilizing transfer learning techniques suppressing irrelevant information reducing missed detections partially occluded targets offering valuable insights global economy expands deep sort algorithms deep sort algorithm become increasingly crucial ship monitoring scenarios model &# 8217 limited ship data deep sort model emphasizing salient features enhanced system achieves demonstrated robust performance improves detection accuracy ship detection model training stable features xlink "> waterway transportation unlinked tracks significant challenges partial convolution logistics sector irregularly shaped iou metric invalid regions growth presents feature extraction false negatives extensive evaluation diou metric detected objects critical reference computational demands artificial intelligence article introduces also reduces address issues
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Description
Summary:	<p>(a) The original YOLO model (b)YOLOv7+SimAM. (c)YOLOv7+SimAM+Pconv.</p>

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