Autonomous object detection dataset: a study on Bangladeshi roadways

Abstract

This research aims to enhance object detection in autonomous driving by testing the three new advanced YOLO models, namely, YOLOv5, YOLOv7, and YOLOv8, on a private dataset proposed specifically for Bangladesh. It is suggested that the dataset include conventional vehicles such as cars, trucks, and buses, nonconventional modes of transport such as rickshaws and CNGs, and complex scenes with poor visibility or high congestion. The contribution of this research involves the creation of a custom dataset that overcomes the deficiencies in other publicly available datasets in terms of improved representation for unconventional vehicles and managing different traffic and weather conditions. YOLOv5, YOLOv7, and YOLOv8 are trained and tested by comparing performances concerning mean Average Precision (mAP), precision, recall, and inference speed. YOLOv8 was the best performer in different difficult conditions related to traffic and even challenge lighting environments, while YOLOv5 was observed to perform more efficiently in the case of real-time applications where the resources are constrained. YOLOv7 strikes a good balance between speed and accuracy, hence is suitable for moderately complex environments. However, all the models were vulnerable to class imbalance and the detection of smaller or occluded objects. Comparing these models with their predecessors, like Faster R-CNN, testifies that the YOLO models perform the best in real time among other models. In general, but especially for YOLOv8, more improvements should be made on dataset representation, occlusion handling, and edge optimization if it is to find a place in autonomous driving systems in the near future. Hence, the research relies on a custom-built private dataset, which effectively captures the dynamic and unstructured environment of traffic in Bangladesh in much finer detail compared to other, earlier datasets.