YOLOV8: An Enhanced Object Detection Model for Distance Estimation - Urvashi Verma (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The rapid evolution of deep learning has transformed computer vision, yet research on leveraging this technology for distance estimation remains limited. Such investigations could greatly benefit various applications, notably anomaly detection. This study introduces an enhanced detection model, YOLOV8-CAW, which Integrates Coordinate Attention and Wise-loU Into the YOLOV8 framework to improve detection accuracy. Incorporating a distance estimation algorithm yields comprehensive outputs, combining detection results with accurate distance calculations. Experimental results demonstrate significant performance enhancements, with improvements in recall (0.4%), precision (2.2%), and Mean Average Precision (mAP) (1.5%) within the 0.5 to 0.95 threshold range while maintaining inference speeds comparable to the baseline model on the PASCAL VOC dataset. Additionally, distance estimation achieves an approximate average accuracy of 90%, indicating promising outcomes. The effective combination of computer vision and separate estimation presents unused roads for viable applications, highlighting the potential of this approach in real-world scenarios.