Real-time object detection for unmanned vehicles in Bangladesh: Dataset, implementation and evaluation

Automated vehicle detection within the advanced application framework of autonomous vehicles significantly enhances road safety compared to human drivers on roads and highways. However, the intelligent identification of road vehicles in a densely populated country like Bangladesh is challenging due to irregular traffic patterns, highly diverse vehicle types, a cluttered environment, and a lack of high-quality datasets. This study proposes a system that utilizes computer vision technology to identify road vehicles with greater speed and accuracy. First, the dataset was collected and organized in Roboflow to identify the 21 classes of Bangladeshi native vehicle images, along with two additional classes for people and animals. Subsequently, the You Only Look Once v5 (YOLOv5) model underwent training on the dataset. This process produced bounding boxes, which were then refined using the non-maximum suppression technique. The loss function complete intersection over union is employed to obtain the accurate regression bounding box of the vehicles. The MS COCO (Microsoft Common Objects in Context) dataset weights are included in the YOLOv5 deep learning network for transfer learning. Finally, Python TensorBoard was used to evaluate and visualize the model's performance. The model was developed and validated on the Google Colab platform. A set of experimental evaluations demonstrate that the proposed method is effective and efficient in recognizing Bangladeshi vehicles. In all test road scenarios, the proposed computer vision system for road vehicle identification achieved 95.8% accuracy and 0.3 ms processing time for 200 epochs. This research could lead to intelligent transportation systems and driverless vehicles in Bangladesh.