计算机与现代化Issue(7)：39-44,6.DOI:10.3969/j.issn.1006-2475.2026.07.006
Violation-YOLO:面向电力作业违章识别的实时目标检测模型
Violation-YOLO:Real-time Object Detection Model for Recognizing Violations in Electric Power Operations
摘要
Abstract
To address the issues of insufficient feature extraction for irregularly shaped and multi-scale targets and the incom-plete coverage of sample categories in power operation violation recognition,this paper proposes a Violation-YOLO detection method based on an improved YOLOv8.Firstly,this method proposes a Deformable-C2f(DC2f)module,which incorporates de-formable convolution and depthwise separable convolution to enhance the feature representation of irregularly shaped targets while reducing the number of parameters.Secondly,the proposed method proposes an Adaptive Multiscale Channel Attention(AMCA)module that integrates multi-scale depthwise separable convolution,adaptive convolution,and channel attention mechanisms,thereby enhancing the feature extraction capability for targets of various scales with relatively low parameter count.Finally,the proposed method constructs a target detection dataset for power operation violation recognition,named the EOVR-v1.0,which includes several target categories not present in publicly available datasets.On the EOVR-v1.0 dataset,ablation ex-periments verify the effectiveness of the DC2f module and the AMCA module,as well as their combination.Compared with the baseline YOLOv8 model,mAP@0.5 and mAP@0.5-0.95 are improved by 2.6 percentage points and 3.1 percentage points respec-tively,with a slight decrease in the number of parameters and GFLOPS.Comparative experiments with seven mainstream YOLO models show that the method achieves the best results of 87.6％and 62.6％in mAP@0.5 and mAP@0.5-0.95,respectively,and fully meets the real-time requirements of the power operation violation recognition scenario.关键词
深度学习/电力作业违章识别/Violation-YOLO/可变形C2f/自适应多尺度通道注意力/EOVR-v1.0数据集Key words
deep learning/electrical operation violation recognition/Violation-YOLO/deformable-C2f/adaptive multiscale channel attention/EOVR-v1.0 dataset分类
信息技术与安全科学引用本文复制引用
朱建宝,俞鑫春,邓伟超,陈宇,朱华雄,黄玮超..Violation-YOLO:面向电力作业违章识别的实时目标检测模型[J].计算机与现代化,2026,(7):39-44,6.基金项目
国家自然科学基金面上项目(62076223) （62076223）
国网南通供电公司2024年面向生产一线科技项目(B71080234Z02) （B71080234Z02）
