Industrial few-shot fractal object detection

In practical industrial visual inspection tasks, foreign object data are difficult to collect and accumulate, hence few-shot object detection has gradually become the focus of research. It has been observed that industrial foreign objects are often different from natural data and are always fractal objects. Its form is a rough or fragmented geometric shape, and its features are relatively monotonous and difficult to distinguish. Optimization-based meta-learning is a powerful approach to few-shot learning. It updates model weights through a parameter optimization strategy enabling more efficient learning when faced with new tasks with few samples. Therefore, we proposed a gradient scout strategy, which used the intelligent optimization idea to optimize the meta-training outer-loop parallel gradient optimization method to improve the training effect of few-shot fractal object detection. Meanwhile, we proposed a fractal information amplified learning module, which could improve the detection ability of few-shot fractal objects more quickly under the same training period. They formed FLGS (fractal information amplified learning with gradient scout), which was deployed at zero cost. YOLOv7 was advanced to a new industrial fractal object detection model under FLGS. The experimental results on the IGBT surface foreign object dataset showed that our gradient scout strategy was superior to the other eight few-shot meta-learning algorithms. FLGS significantly accelerated the improvement of fractal object detection ability and maintained a high-level mean average precision.