Lightweight Neural Networks for Pedestrian Detection in Intelligent Vehicles

Abstract

Most actual intelligent vehicles (IV) are powered by a variety of sensors and cameras. Vision-based applications for IV mainly require visual information. In this paper, the authors introduce a pedestrian detection application used for pedestrian safety. The authors proposed a deep fully convolutional neural network (DFCNN) for pedestrian detection. The proposed model is suitable for mobile implementation. To do this, the authors propose to build lightweight blocks using convolution layers, and replace pooling layers and fully connected layers with convolution layers. Training and testing of the proposed DFCNN model for pedestrian detection were performed using the Caltech dataset. The proposed DFCNN has achieved 85% of average precision and an inference speed of 30 FPS. The reported results have demonstrated the robustness of the proposed DFCNN for pedestrian detection. The achieved performance was low computation complexity and high performance.