Lightweight research based on YOLOv5 grenade detection algorithm

DOI:	10.11809/bqzbgcxb2023.06.005
Keywords:	grenade detection; YOLOv5 algorithm; Ghost module; attention mechanism; complex environment
Abstract:	Grenade detection is a key task of unmanned explosive ordnance disposal (EOD). YOLOv5 algorithm has a high accuracy and a good real time performance in grenade detection, but is not lightweight enough. In a complex environment where the projectile body is partially occluded or the background is cluttered, the algorithm does not have a high grenade recognition accuracy. In this view, based on a combination of Ghost module and Coordinate Attention (CA) module, this paper proposes an improved YOLOv5 GA algorithm. After the experiments on the self made grenade data set, the parameters of the improved algorithm decrease by 50%, the detection accuracy decreases by 1%, and the detection speed increases by 3 ms. The recognition effect of the occluded grenades is significantly improved, which can better meet the practical application requirements.
Published:	2023-06-28
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