Definition of a reference standard for performance evaluation of autonomous vehicles real-time obstacle detection and distance estimation in complex environments

Effective obstacle detection is vital for autonomous agricultural robots in complex environments. Due to tree occlusion and varying lighting conditions, current systems need help with misidentifying obstacles, particularly in orchards. This study presents a novel methodology for real-time obstacle detection and distance estimation, improving the YOLOv8n architecture with the Convolutional Block Attention Module (CBAM) to improve feature representation. Additionally, the Non-Maximum Suppression (NMS) technique is enhanced with Soft DIou-NMS to minimize redundant detections for overlapping objects. A comprehensive dataset of common orchard obstacles was utilized for evaluation, measuring performance through Precision (P), Recall (R), F1-score, and confusion matrices. Experimental results showed that the improved YOLOv8n model outperforms baseline models, including YOLOv5s and various YOLOv8 variants (n, s, m, l, x), achieving a 92.7 % mean Average Precision (mAP) at IoU 0.5 and an F1-score of 87 % at a confidence threshold of 42.1 %. Inference time was reduced to 3.3 ms (ms), and the model size to 20.1 MB. The model was evaluated under various lighting conditions, achieving mAP-50 scores of 96.5 % in daylight and 91.4 % in low light, with minimal performance drops. Testing across distance ranges (close: 1–3 m, medium: 3–7 m, long: 7–12 m) showed strong performance in close and medium ranges. Real-time validation indicated a detection time of 20.1 ms per frame, making it suitable for agricultural vehicles. These findings provide a reference standard for obstacle detection and distance estimation in orchard environments, contributing to the safety and autonomy of agricultural vehicles in complex settings.