Detection of Fragment Penetration Effect Based on YOLOv8

RONG Jiangwei; CAO Hongsong; CHU Wenbo; WANG Qingyue; DUAN Mingqing; ZHANG Hao; HE Xiaoping

doi:10.12382/bgxb.2025.0646

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Detection of Fragment Penetration Effect Based on YOLOv8

更新时间：2026-05-06

- Detection of Fragment Penetration Effect Based on YOLOv8
- Acta Armamentarii Vol. 47, Issue 4, Pages: 250646(2026)
- 作者机构：
  
  中北大学机电工程学院，山西太原 030051
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0646
  CLC： TJ061;TJ413.1
- Received：14 July 2025，
  
  Online First：27 January 2026，
  
  Published：2026-04
- 稿件说明：
移动端阅览
RONG Jiangwei, CAO Hongsong, CHU Wenbo, et al. Detection of Fragment Penetration Effect Based on YOLOv8[J]. Acta Armamentarii, 2026, 47(4): 250646.
DOI：

RONG Jiangwei, CAO Hongsong, CHU Wenbo, et al. Detection of Fragment Penetration Effect Based on YOLOv8[J]. Acta Armamentarii, 2026, 47(4): 250646. DOI： 10.12382/bgxb.2025.0646.

摘要

针对靶场工作中人工采集数据效率低、精度差的问题，提出基于图像识别的自动检靶技术，以取代传统人工破片分布统计方法。破片靶孔存在形态多样、分布密集、背景复杂等特性，叠加靶板锈蚀和风化等噪声干扰，导致边缘轮廓分割精度受限，为此提出金属靶破片靶孔分割模型（YOLOv8-Target plate Fragment perforation Segmentation，YOLOv8-TFS）。在YOLOv8n-seg模型的基础上，增加微小靶孔检测层以提升模型对于不同尺寸靶孔的特征提取能力；优化跨阶段链接的特征融合网络结构；引入双通路自适应特征加权特征融合模块，强化特征表达并抑制背景噪声；设计多路径感受野注意力分割头，提高模型对靶孔特征的整合输出能力。试验结果表明，YOLOv8-TFS模型在自制数据集上的掩码精确率、召回率和mAP@0.5%分别达到85.2%、74.3%和73.8%，较原始模型分别提升11.6%、10.9%、9.0%，有效提高了靶孔分割的准确性。基于分割结果构建的自动检靶系统，通过空间矩计算与坐标转换实现靶孔面积及质心坐标的精确解算。与人工检靶相比，数量平均绝对偏差为1.07个，面积偏差控制在5%以内，验证了检靶方法的可靠性与高精度特性，为战斗部毁伤效能评估提供了高效的技术支撑。

Abstract

Aiming at the problems of low efficiency and poor accuracy of manual data collection in shooting range

an automatic target detection technology based on image recognition is proposed to replace the traditional manual statistical method of fragment distribution. The target holes have characteristics such as diverse shapes

dense distribution

and complex background

and are further influenced by the rust and weathering of target plate

which limits the accuracy of edge contour segmentation. Therefore

this paper proposes a target plate fragment perforation segmentation model (YOLOv8-Target plate Fragment perforation Segmentation

YOLOv8-TFS) .Based on YOLOv8 model

a micro-target hole detection layer is added to enhance the model's ability to extract the features of different-sized target holes. The feature fusion network structure of cross-stage connections is optimized

and a dual-path adaptive feature weighting feature fusion module is introduced to strengthen the feature expression and suppress the background noise. A multi-path receptive field attention segmentation head is designed to improve the model's ability to integrate and output the target hole features. Experimental results show that the mask precision

recall rate

and mAP@0.5% of YOLOv8-TFS model on the self-made dataset reach 85.2%

74.3%

and 73.8%

respectively

which are 11.6%

10.9%

and 9.0% higher than those of the original model

effectively improving the accuracy of target hole segmentation. The automatic target detection system constructed based on the segmentation results accurately calculates the area and centroid coordinates of the target holes through spatial moment calculation and coordinate transformation. Compared with manual target detection

the average absolute deviation in quantity is 1.07

and the area deviation is controlled within 5%.This verifies the reliability and high-precision characteristics of the target detection method

and providing efficient technical support for the assessment of warhead damage effectiveness.

关键词

Keywords

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