What is YOLOv6? A Deep Insight into the Object Detection Model
It addresses the need for efficient real-time object detection in applications like autonomous driving and surveillance, representing an incremental improvement over previous YOLO versions.
This work explores the YOLOv6 object detection model, achieving high performance with YOLOv6-N at 37.5% AP and 1187 FPS, and YOLOv6-S at 45.0% AP and 484 FPS on the COCO dataset, outperforming several comparable models.
This work explores the YOLOv6 object detection model in depth, concentrating on its design framework, optimization techniques, and detection capabilities. YOLOv6's core elements consist of the EfficientRep Backbone for robust feature extraction and the Rep-PAN Neck for seamless feature aggregation, ensuring high-performance object detection. Evaluated on the COCO dataset, YOLOv6-N achieves 37.5\% AP at 1187 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S reaches 45.0\% AP at 484 FPS, outperforming models like PPYOLOE-S, YOLOv5-S, YOLOX-S, and YOLOv8-S in the same class. Moreover, YOLOv6-M and YOLOv6-L also show better accuracy (50.0\% and 52.8\%) while maintaining comparable inference speeds to other detectors. With an upgraded backbone and neck structure, YOLOv6-L6 delivers cutting-edge accuracy in real-time.