The training results of WA-YOLO and YOLOv8 on the custom blasting site dataset are presented, where (a) shows the mAP<sub>0.5</sub> comparison, and (b) illustrates the box loss training comparison.

The training results of WA-YOLO and YOLOv8 on the custom blasting site dataset are presented, where (a) shows the mAP<sub>0.5</sub> comparison, and (b) illustrates the box loss training comparison.

<p>The training results of WA-YOLO and YOLOv8 on the custom blasting site dataset are presented, where (a) shows the mAP<sub>0.5</sub> comparison, and (b) illustrates the box loss training comparison.</p>

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Bibliographic Details
Main Author:	LinNa Li (5665885) (author)
Other Authors:	Han Gao (486886) (author), JunYi Lu (21162047) (author), XiaoXiao Xu (21162050) (author)
Published:	2025
Subjects:	Biochemistry Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified parallel attention mechanism novel convolutional block new convolutional block iou loss function improved algorithm based model &# 8217 improved model achieved varying aspect ratios experimental results show depthwise separable convolution yolov8 </ p improving feature extraction blasting sites based integrate wavelet convolution average precision increase separable convolution blasting sites model uses varying scales results indicate recall increase blasting safety voc2012 dataset scale convolutions paper proposes overlap calculations object scales neck network irregular shapes effective solution detonator wires detect objects critical issues complex environment complex backgrounds c2f module built dataset bounding boxes
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