Object detection on embedded systems for traffic in asian countries

Abstract

In this paper, we present a novel embedded deep learning solution for traffic object detection. Considering the memory, computing speed, and environmental requirements in the MediaTek Dimensity 1000 Series embedded device, it is worth mentioning that we are the first one to re-implement and propose an efficient object detection algorithm based on You Only Look Once version 5 (YOLOv5) to address this issue in TensorFlow framework. The backbone of our network is mainly constructed by Cross Stage Partial (CSP) modules, which significantly boost the accuracy of our model and keep the model lightweight. Besides, to enhance the prediction effectiveness, we propose to combine the official training dataset and several external open datasets as our comprehensive training data. We also adopt multiple data augmentation techniques in the training phase, making the model learn a stronger feature extraction ability for various object categories. According to the results of extensive experiments and the final competition scores, our solution can get a not bad performance under the condition of low parameters and complexity. Our team is the third-place winner in Embedded Deep Learning Object Detection Model Compression Competition in ACM International Conference on Multimedia Retrieval 2021.