Deep Learning-Based Traffic Light Detection in a Custom Embedded Hardware Platform for ADAS Applications

Abstract

Automotive Driver Assistance Systems (ADAS) applications are currently an intensive field of study and innovation. The development of an ADAS is a multidisciplinary task involving electronic hardware design, advanced software implementation, safety considerations and many more. Building an ADAS application implies some challenges that are addressed in this paper. Firstly, all ADAS applications run on highly specific hardware devices embedded in the car with limited computation resources. In this work a novel embedded platform, iADASys, is developed and tested. The platform integrates the elements required to implement an artificial vision based ADAS application such as high performance processor with Deep Learning (DL) computation co-processors or multi-channel high resolution video streaming hardware. Secondly, this work implements an artificial vision application for traffic light detection based on deep neural networks. The model selected in this work is SSD_Mobilenet_V1 and it was trained using Bosch Small Traffic Light (BSTL) dataset. To fulfill real time requirement, the model image input resolution was maintained low at $300\times 300$ pixel. However, the small object size in the dataset together with low resolution lead to poor detection performance. This situation was addressed by fine tuning the model training hyperparameters related to detection scales and aspect ratios. Lastly, the model is deployed in the hardware platform and its performance is measured. Model inference is executed on a specialized mathematical co-processor obtaining the required real time response. The object detection performance is also measured, obtaining promising results.

Keywords

• ADAS
• deep learning
• object detection
• neural network hardware