Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are imp...Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.展开更多
针对玉米杂草识别过程中因光照变化导致识别精确度低及漏检问题,该研究以幼苗期玉米及其伴生杂草为研究对象,设计一种基于WEED-YOLOv10的玉米杂草检测方法。首先,通过无人机快速采集田间高分辨率图像构建了玉米杂草数据集;其次,以YOLOv...针对玉米杂草识别过程中因光照变化导致识别精确度低及漏检问题,该研究以幼苗期玉米及其伴生杂草为研究对象,设计一种基于WEED-YOLOv10的玉米杂草检测方法。首先,通过无人机快速采集田间高分辨率图像构建了玉米杂草数据集;其次,以YOLOv10n为基线网络,将骨干网络替换为ConvNeXtV2以增强特征提取能力;继而,为避免因模块拼接可能带来的信息冗余或丢失问题提升对光照干扰的鲁棒性,嵌入CBAM注意力机制;然后,引入SlimNeck结构优化网络计算效率,有效平衡了模型计算资源消耗与特征表征能力;最后,使用Focaler-EIoU损失函数进一步提高模型定位精度。试验结果表明,WEED-YOLOv10在精确率、召回率、mAP@50、mAP@50:95和F1分数上分别达到85.4%、88.1%、90.9%、48.5%和86.7%,较基准模型分别提升了2.4、2.9、3.5、7.0、2.6个百分点,各项精度指标均优于其他对比模型,部署在NVIDIA Jetson orin NX上的图片推理速度达到28.7帧/s,实现了检测速度与精度的平衡。进一步地,基于WEED-YOLOv10开发对靶喷药系统,该系统实时捕捉并解析来自模型的识别信号,实现对除草喷施装置的精准调控。田间试验结果显示,对靶喷药系统施药准确率为93.7%,喷洒覆盖率为90.5%,对靶偏差为1.45cm,杂草实时检测速度为20.1帧/s,实现了自动化的玉米田间除草作业。该研究为复杂光照场景下农田杂草治理提供了可靠的技术方案,对推动农业智能化作业具有重要意义。展开更多
基金supported by University of Malaya and Ministry of High Education-Malaysia via Fundamental Research Grant Scheme No.FRGS/1/2023/TK10/UM/02/3.
文摘Accurate and real-time traffic-sign detection is a cornerstone of Advanced Driver-Assistance Systems(ADAS)and autonomous vehicles.However,existing one-stage detectors miss distant signs,and two-stage pipelines are impractical for embedded deployment.To address this issue,we present YOLO-SMM,a lightweight two-stage framework.This framework is designed to augment the YOLOv8 baseline with three targeted modules.(1)SlimNeck replaces PAN/FPN with a CSP-OSA/GSConv fusion block,reducing parameters and FLOPs without compromising multi-scale detail.(2)The MCA model introduces row-and column-aware weights to selectively amplify small sign regions in cluttered scenes.(3)MPDIoU augments CIoU loss with a corner-distance term,supplying stable gradients for sub-20-pixel boxes and tightening localization.An evaluation of YOLO-SMMon the German Traffic Sign Recognition Benchmark(GTSRB)revealed that it attained 96.3% mAP50 and 93.1% mAP50-90 at a rate of 90.6 frames per second(FPS).This represents an improvement of+1.0% over previous performance benchmarks.Them APat 64×64 resolution was found to be 50% of the maximum attainable value,with an FPS of+8.3 when compared to YOLOv8.This result indicates superior performance in terms of accuracy and speed compared to YOLOv7,YOLOv5,RetinaNet,EfficientDet,and Faster R-CNN,all of which were operated under equivalent conditions.
文摘针对玉米杂草识别过程中因光照变化导致识别精确度低及漏检问题,该研究以幼苗期玉米及其伴生杂草为研究对象,设计一种基于WEED-YOLOv10的玉米杂草检测方法。首先,通过无人机快速采集田间高分辨率图像构建了玉米杂草数据集;其次,以YOLOv10n为基线网络,将骨干网络替换为ConvNeXtV2以增强特征提取能力;继而,为避免因模块拼接可能带来的信息冗余或丢失问题提升对光照干扰的鲁棒性,嵌入CBAM注意力机制;然后,引入SlimNeck结构优化网络计算效率,有效平衡了模型计算资源消耗与特征表征能力;最后,使用Focaler-EIoU损失函数进一步提高模型定位精度。试验结果表明,WEED-YOLOv10在精确率、召回率、mAP@50、mAP@50:95和F1分数上分别达到85.4%、88.1%、90.9%、48.5%和86.7%,较基准模型分别提升了2.4、2.9、3.5、7.0、2.6个百分点,各项精度指标均优于其他对比模型,部署在NVIDIA Jetson orin NX上的图片推理速度达到28.7帧/s,实现了检测速度与精度的平衡。进一步地,基于WEED-YOLOv10开发对靶喷药系统,该系统实时捕捉并解析来自模型的识别信号,实现对除草喷施装置的精准调控。田间试验结果显示,对靶喷药系统施药准确率为93.7%,喷洒覆盖率为90.5%,对靶偏差为1.45cm,杂草实时检测速度为20.1帧/s,实现了自动化的玉米田间除草作业。该研究为复杂光照场景下农田杂草治理提供了可靠的技术方案,对推动农业智能化作业具有重要意义。
