WA-YOLO: An explosive material detection algorithm for blasting sites based on YOLOv8
LinNa Li, Han Gao, JunYi Lu, XiaoXiao Xu
TL;DR
This paper introduces WA-YOLO, a new algorithm for detecting explosive materials in blasting sites, improving accuracy and robustness in complex environments.
Contribution
The novel WSDConv block and modified CSP structure with parallel attention enhance detection of pyrotechnics in challenging blasting conditions.
Findings
WA-YOLO achieved a 12.6% average precision increase on a self-built dataset.
Detonator detection improved by 8.3% average precision.
Model showed 1.6% average precision increase on the VOC2012 dataset.
Abstract
Pyrotechnic detection has always been one of the critical issues in blasting safety. Due to the complex environment of blasting sites, irregular detonator wire postures, and the differences in object scales, making the detection of pyrotechnics more challenging. To address these challenges, this paper proposes an improved algorithm based on a multi-scale parallel attention mechanism and wavelet-separable convolution, called WA-YOLO. First, we integrate wavelet convolution into depthwise separable convolution and propose a novel convolutional block (WSDConv, Wavelet Separable Depthwise Convolution). This new convolutional block is added to the model’s backbone, improving feature extraction while also lowering computational parameters. Furthermore, we introduce an improved Cross Stage Partial (CSP) structure by combining multi-scale convolutions with a parallel attention mechanism,…
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Taxonomy
TopicsGeophysical Methods and Applications · Earthquake Detection and Analysis · Synthetic Aperture Radar (SAR) Applications and Techniques