PDF(Pubmed)
Abstract：
Wearing helmets is essential in two-wheeler traffic to reduce the incidence of injuries caused by accidents. We present FB-YOLOv7, an improved detection network based on the YOLOv7-tiny model. The objective of this network is to tackle the problems of both missed detection and false detection that result from the difficulties in identifying small targets and the constraints in equipment performance during helmet detection. By applying an enhanced Bi-Level Routing Attention, the network can improve its capacity to extract global characteristics and reduce information distortion. Furthermore, we deploy the AFPN framework and effectively resolve information conflict using asymptotic adaptive feature fusion technology. Incorporating the EfficiCIoU loss significantly improves the prediction box\'s accuracy. Experimental trials done on specific datasets reveal that FB-YOLOv7 attains an accuracy of 87.2% and 94.6% on the mean average precision (mAP@.5). Additionally, it maintains a high level of efficiency with frame rates of 129 and 126 frames per second (FPS). FB-YOLOv7 surpasses the other six widely-used detection networks in terms of detection accuracy, network implementation requirements, sensitivity in detecting small targets, and potential for practical applications.
摘要：
戴头盔在两轮车交通中至关重要，以减少事故造成的伤害。我们介绍了FB-YOLOv7，这是一种基于YOLOv7-tiny模型的改进检测网络。该网络的目的是解决由于识别小目标的困难以及头盔检测期间设备性能的限制而导致的漏检和错误检测的问题。通过应用增强的双层路由注意力，该网络可以提高其提取全局特征的能力，减少信息失真。此外，我们部署了AFPN框架，并使用渐近自适应特征融合技术有效地解决了信息冲突。纳入EfficiCIoU损失显著提高了预测框的准确性。在特定数据集上进行的实验试验表明，FB-YOLOv7在平均精度(mAP@.5)上达到87.2%和94.6%的准确度。此外,它以每秒129和126帧（FPS）的帧速率保持高效率。FB-YOLOv7在检测精度方面超过了其他六个广泛使用的检测网络，网络实施要求，检测小目标的灵敏度，和实际应用的潜力。
