I am developing a computer vision system for a manufacturing line to identify surface cracks on metal parts in real-time. I need high accuracy but the latency must be under 30ms per frame. Should I stick with a two-stage detector like Faster R-CNN for precision, or is YOLOv8's single-stage speed sufficient?
3 answers
For industrial defect detection on a moving line, YOLOv8 is almost certainly the better choice. In 2024, the gap in accuracy between single-stage and two-stage detectors has narrowed significantly. YOLOv8 uses a CSPDarknet backbone and a decoupled head that allows it to achieve mAP scores comparable to Faster R-CNN while being 5 to 10 times faster. With a latency requirement of 30ms, Faster R-CNN will likely struggle unless you have an extremely powerful A100 GPU. I recommend starting with YOLOv8m (medium) and using TensorRT for deployment to ensure you stay well within your millisecond budget.
This is a great point, but how do you handle very small cracks? In my experience, YOLO sometimes struggles with tiny objects compared to the Region Proposal Network in Faster R-CNN.
YOLOv8 is definitely the industry standard for speed. Make sure you use synthetic data augmentation during training to help the model recognize cracks under different lighting conditions.
I agree with Kimberly. Augmentation like mosaic and mixup is built into YOLOv8 and is essential when you have a limited dataset of actual defective parts.
You're right, Jeffrey, small object detection is the classic YOLO weakness. To fix this in YOLOv8, we use a "Slicing" technique (SAHI) or increase the input resolution to 1024 pixels. Another trick is to modify the neck of the network to include more high-resolution feature maps. This allows the model to retain fine-grained details of those tiny cracks without the massive latency hit of a two-stage detector. It requires some custom hyperparameter tuning during training, but it’s a standard workaround in high-precision manufacturing.