The development of autonomous driving technology must be inseparable from pedestrian detection. Because of the fast speed of the vehicle, the accuracy and real-time performance of the pedestrian detection algorithm are very important. YOLO, as an efficient and simple one-stage target detection method, is often used for pedestrian detection in various environments. However, this series of detectors face some challenges, such as excessive computation and undesirable detection rate when facing occluded pedestrians. In this paper, we propose an improved lightweight YOLOv5 model to deal with these problems. This model can achieve better pedestrian detection accuracy with fewer floating-point operations (FLOPs), especially for occluded targets. In order to achieve the above goals, we made improvements based on the YOLOv5 model framework and introduced Ghost module and SE block. Furthermore, we designed a local feature fusion module (FFM) to deal with occlusion in pedestrian detection. To verify the validity of our method, two datasets, Citypersons and CUHK Occlusion, were selected for the experiment. The experimental results show that, compared with the original yolov5s model, the average precision (AP) of our method is significantly improved, while the number of parameters is reduced by 27.9% and FLOPs are reduced by 19.0%.

翻译：自动驾驶技术的发展必然离不开行人检测。由于车辆行驶速度快，行人检测算法的准确性和实时性至关重要。YOLO作为一种高效简洁的单阶段目标检测方法，常被用于各种环境下的行人检测。然而，该系列检测器在面对遮挡行人时面临一些挑战，例如计算量过大、检测率不理想等。本文提出一种改进的轻量化YOLOv5模型来解决这些问题。该模型能够以更少的浮点运算次数（FLOPs）实现更好的行人检测精度，尤其针对遮挡目标。为实现上述目标，我们在YOLOv5模型框架基础上进行改进，引入了Ghost模块和SE注意力模块。此外，我们设计了一个局部特征融合模块（FFM）来处理行人检测中的遮挡问题。为验证方法的有效性，实验选取了Citypersons和CUHK Occlusion两个数据集。实验结果表明，与原始yolov5s模型相比，本方法的平均精度（AP）显著提升，同时参数量减少27.9%，FLOPs降低19.0%。