Enhanced Object Detection Based on YoloV8+EAC for an Autonomous Vehicle

The ability of autonomous vehicles to recognize different objects in their natural environments, such as cars, trucks, pedestrians, bicyclists, and traffic lights, is made possible in large part by object detection. Enhanced object detection in self-driving traffic with the modified YOLOv8 architecture model is proposed. The attention mechanism is one of improve method detection object performance. The Efficient Attention Channel (EAC) integrates the attention mechanism into the original YOLOv8 architecture. The improved YOLOv8+EAC algorithm is tested through the self-driving traffic detection dataset to identify 10 class objects such as biker, car, traffic light-green, traffic light-green left, traffic-light red, traffic-light red left, traffic-light yellow and truck. The results show a precision confidence of 95.6% and mAP value of 72.4%. In addition, the comparative performance is superior to other architecture models. The proposed method indeed improves the accuracy of recognizing traffic signs in complex scenarios requirements of intelligent vehicles for traffic sign recognition tasks.