Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection mo...Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.展开更多
为保证配电网线路的安全与稳定,及时准确地识别出故障绝缘子至关重要。针对配电架空线路巡检图像中绝缘子缺陷目标小、背景复杂造成的漏检率和误检率高的问题,本文提出一种改进型Yolov10(you only look once version 10)的绝缘子缺陷检...为保证配电网线路的安全与稳定,及时准确地识别出故障绝缘子至关重要。针对配电架空线路巡检图像中绝缘子缺陷目标小、背景复杂造成的漏检率和误检率高的问题,本文提出一种改进型Yolov10(you only look once version 10)的绝缘子缺陷检测算法。首先,采用正负样本混合增强策略,丰富样本类型,降低非故障绝缘子的误报率。其次,在Yolov10的主干网络中集成可变形卷积网络(deformable convolution networks version 4,DCNv4)提高对不同形态缺陷的适应性,同时在网络颈部引入双向特征金字塔动态融合特征,减少小目标特征遗漏。最后,使用基于距离交并比(distance intersection over union,DIoU)的非极大值抑制(non-maximum suppression,NMS),即DIoU_NMS后处理技术,改善因线路布局复杂导致目标遮挡带来的检测框错误抑制情况。实验表明,该方法在绝缘子缺陷小目标的识别率和误检效果方面均有所改善,平均精度均值(mean average precision,mAP)达到了83.2%,提高了检测的准确性和可靠性。展开更多
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.IPP:172-830-2025.
文摘Reliable detection of traffic signs and lights(TSLs)at long range and under varying illumination is essen-tial for improving the perception and safety of autonomous driving systems(ADS).Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions.To overcome these limitations,this research presents FED-YOLOv10s,an improved and lightweight object detection framework based on You Only look Once v10(YOLOv10).The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations,an Efficient Multiscale Attention(EMA)mechanism to improve TSL-invariant feature extraction,and a deformable Convolution Networks v4(DCNv4)module to enhance multiscale spatial adaptability.Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy,attaining an F1-score of 91.8%,and mAP@0.5 of 95.1%,while reducing parameters to 8.13 million.Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision,recall,and mAP.These results highlight FED-YOLOv10s as a robust,efficient,and deployable solution for intelligent traffic perception in ADS.
文摘为保证配电网线路的安全与稳定,及时准确地识别出故障绝缘子至关重要。针对配电架空线路巡检图像中绝缘子缺陷目标小、背景复杂造成的漏检率和误检率高的问题,本文提出一种改进型Yolov10(you only look once version 10)的绝缘子缺陷检测算法。首先,采用正负样本混合增强策略,丰富样本类型,降低非故障绝缘子的误报率。其次,在Yolov10的主干网络中集成可变形卷积网络(deformable convolution networks version 4,DCNv4)提高对不同形态缺陷的适应性,同时在网络颈部引入双向特征金字塔动态融合特征,减少小目标特征遗漏。最后,使用基于距离交并比(distance intersection over union,DIoU)的非极大值抑制(non-maximum suppression,NMS),即DIoU_NMS后处理技术,改善因线路布局复杂导致目标遮挡带来的检测框错误抑制情况。实验表明,该方法在绝缘子缺陷小目标的识别率和误检效果方面均有所改善,平均精度均值(mean average precision,mAP)达到了83.2%,提高了检测的准确性和可靠性。
