Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus result...Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection.展开更多
精准识别和定位飞机目标是航空安全和信息化战争胜利的关键,针对传统飞机目标识别抗干扰性差,对遮挡、光照、尺度敏感难应对复杂场景需求的问题,提出了一种基于改进YOLOv5的飞机目标检测算法。通过IOU-NWD Similarity Metric for Boundi...精准识别和定位飞机目标是航空安全和信息化战争胜利的关键,针对传统飞机目标识别抗干扰性差,对遮挡、光照、尺度敏感难应对复杂场景需求的问题,提出了一种基于改进YOLOv5的飞机目标检测算法。通过IOU-NWD Similarity Metric for Bounding Boxes策略解决了IOU机制对飞机小目标的标签分配歧义问题;使用GFPN based on NLnet模块完成了“跨层”与“跨尺度”的自适应融合,更加丰富和具有代表性的特征信息;使用soft-NMS方法解决了在目标密集区域中飞机小目标存在的漏检问题。在飞机数据集上进行实验,结果表明:与原始YOLOv5相比,改进后的模型在Precision、Recall、mAP0.5、mAP0.5:0.95分别提高了1.9%、10.4%、3.6%和5.8%。该算法通过针对性的网络调整和模块迁移来提高模型对小型和遮挡的飞机目标的检测效果,并通过实验验证了该算法的优越性,实验结果表明,AIR-YOLO在检测精度和鲁棒性方面优于YOLOv5,解决了原始YOLOv5算法的小飞机目标误检的问题。展开更多
基金Supported by the Science Foundation of the Shaanxi Provincial Department of Science and Technology,General Program-Youth Program(2022JQ-695)the Scientific Research Program Funded by Education Department of Shaanxi Provincial Government(22JK0378)+1 种基金the Talent Program of Weinan Normal University(2021RC20)the Educational Reform Research Project(JG202342)。
文摘Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection.
文摘精准识别和定位飞机目标是航空安全和信息化战争胜利的关键,针对传统飞机目标识别抗干扰性差,对遮挡、光照、尺度敏感难应对复杂场景需求的问题,提出了一种基于改进YOLOv5的飞机目标检测算法。通过IOU-NWD Similarity Metric for Bounding Boxes策略解决了IOU机制对飞机小目标的标签分配歧义问题;使用GFPN based on NLnet模块完成了“跨层”与“跨尺度”的自适应融合,更加丰富和具有代表性的特征信息;使用soft-NMS方法解决了在目标密集区域中飞机小目标存在的漏检问题。在飞机数据集上进行实验,结果表明:与原始YOLOv5相比,改进后的模型在Precision、Recall、mAP0.5、mAP0.5:0.95分别提高了1.9%、10.4%、3.6%和5.8%。该算法通过针对性的网络调整和模块迁移来提高模型对小型和遮挡的飞机目标的检测效果,并通过实验验证了该算法的优越性,实验结果表明,AIR-YOLO在检测精度和鲁棒性方面优于YOLOv5,解决了原始YOLOv5算法的小飞机目标误检的问题。
