WA-YOLO: An explosive material detection algorithm for blasting sites based on YOLOv8.

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 Partia

SUBMITTER: Li L 

PROVIDER: S-EPMC12013926 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature