The growth in the number of vehicles in the world makes it hard to safely share the environment with pedestrians. Pedestrian’s safety is an important task that needs to be granted in the traffic environment. New cars are equipped with advanced driver assistance systems (ADAS) with a variety of applications. Pedestrian detection application is one of the most important applications for an ADAS that needs to be enhanced. In this paper, we propose a pedestrian detection system to be implemented in an ADAS. The proposed system is based on convolutional neural networks thanks to its performance when solving computer vision applications. On the other side, the proposed system ensures real-time processing and high detection performance. The proposed system will be designed by tacking the advantage of building lightweight convolution blocks and model compression techniques to ensure an embedded implementation. Those blocks will guarantee high precision and fast processing speed. To train and evaluate the proposed system, we used the Caltech dataset. The evaluation of the proposed system resulted in 87% of mean average precision and an inference speed of 35 frames per second.

世界上车辆数量的增长使得难以与行人安全共享环境。行人的安全是交通环境中必须考虑的一项重要任务。新车配备了具有多种应用的高级驾驶员辅助系统（ADAS）。行人检测应用程序是需要增强的ADAS最重要的应用程序之一。在本文中，我们提出了要在ADAS中实施的行人检测系统。所提出的系统基于卷积神经网络，这要归功于它在解决计算机视觉应用时的性能。另一方面，所提出的系统确保了实时处理和高检测性能。拟议的系统将通过构建轻量级卷积块和模型压缩技术的优势来设计，以确保嵌入式实现。这些块将确保高精度和快速的处理速度。为了训练和评估提出的系统，我们使用了Caltech数据集。对所提出系统的评估得出平均平均精度为87％，推理速度为每秒35帧。