DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA

Abstract. Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations.