Simultaneous Road Objects and Lane Detection Models in Autonomous Vehicles

Poor road boundary lanes and detections of road objects have been identified as some of the serious causes of road accidents, in both conventional and autonomous driving. Therefore, it is critical to develop models that could help autonomous vehicles' perception systems while accurately identifying and locating road objects from images and video frames. However, the existing models face a series of challenges due to the highly complex nature of the road traffic scene and the influence of various road objects on the manoeuvring. Most of the existing models cannot simultaneously detect all the major road objects, with some, either detecting lanes or detecting some of the road objects. To address these gaps, the combined road objects and lane detection model was developed using the You Only Look Once (YOLO) algorithm. As a first step, a model was developed to detect road objects only and the results were compared with existing studies. Next, another model was developed to detect road lanes based on YOLOv8 capability. Finally, an improved YOLOv8 model was developed to simultaneously detect road objects and lanes. To achieve this, the YOLOv8 model was tuned and optimised using various optimization approaches considering several hyperparameters such as activation functions and regularisation methods. Further, the effect of augmentation was investigated using three techniques; cut-out, rotation and rotation with noise. Also, the effect of the data stream on the performance of the model was investigated based on the obtained hyperparameter. The relevant performance metrics such as precision, F1, and recall were deployed. In addition, mean average precision calculated at an intersection over union (IoU) threshold of 0.5 and 0.95 was reported to assess the model's detection capabilities. The results from this study were further compared with some existing studies such as Feature Pyramid Networks, Task-aligned One-stage Object Detection, Dynamic R-CNN Probabilistic Anchor Assignment with IoU Prediction, Sparse R-CNN and CenterNet to demonstrate the contribution of the model. Further, the performance of the models based on different dataset (Curated data, COCO, and KITTI) showed that curated data outperformed others across all the performance metrics. Notably, curated data has the most promising results with precision, recall and F1 score of 0.68, 0.61, and 0.64, respectively. The success of the curated dataset highlights the significance of tailoring datasets to the specific nuances of the targeted application domain. Finally, the conclusion and recommendations were made based on the findings from the study.