Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments

Abstract

In order to reach the highest level of automation, autonomous vehicles (AVs) are required to be aware of surrounding objects and detect them even in adverse weather. Detecting objects is very challenging in sandy weather due to characteristics of the environment, such as low visibility, occlusion, and changes in lighting. In this paper, we considered the You Only Look Once (YOLO) version 5 and version 7 architectures to evaluate the performance of different activation functions in sandy weather. In our experiments, we targeted three activation functions: Sigmoid Linear Unit (SiLU), Rectified Linear Unit (ReLU), and Leaky Rectified Linear Unit (LeakyReLU). The metrics used to evaluate their performance were precision, recall, and mean average precision (mAP). We used the Detection in Adverse Weather Nature (DAWN) dataset which contains various weather conditions, though we selected sandy images only. Moreover, we extended the DAWN dataset and created an augmented version of the dataset using several augmentation techniques, such as blur, saturation, brightness, darkness, noise, exposer, hue, and grayscale. Our results show that in the original DAWN dataset, YOLOv5 with the LeakyReLU activation function surpassed other architectures with respect to the reported research results in sandy weather and achieved 88% mAP. For the augmented DAWN dataset that we developed, YOLOv7 with SiLU achieved 94% mAP.