DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects

Abstract

Industrial defect detection is of great significance to ensure the quality of industrial products. The surface defects of industrial products are characterized by multiple scales, multiple types, abundant small objects, and complex background interference. In particular, small object detection of multiscale defects under complex background interference poses significant challenges for defect detection tasks. How to improve the algorithm’s ability to detect industrial defects, especially in enhancing the detection capabilities of small-sized defects, while ensuring that the inference speed is not overly affected is a long-term prominent challenge. Aiming at achieving accurate and fast detection of industrial defects, this paper proposes an anchor-free network with DsPAN for small object detection. The core of this method is to propose a new idea i.e., feature enhancement in the feature fusion network for the feature information of small-size objects. Firstly, anchor-free YOLOv8 is adopted as the basic framework for detection to eliminate the affections of hyperparameters related to anchors, as well as to improve the detection capability of multi-scale and small-size defects. Secondly, considering the PAN path (top layer of neural networks for feature fusion) is more task-specific, we advocate that the underlying feature map of the PAN path is more vulnerable to small object detection. Hence, we in-depth study the PAN path and point out that the standard PAN will encounter several drawbacks caused by losing local details and position information in deep layer. As an alternative, we propose a lightweight and detail-sensitive PAN (DsPAN) for small object detection of multiscale defects by designing an attention mechanism embedded feature transformation module(LCBHAM) and optimizing the lightweight implementation. Our proposed DsPAN is very easy to be incorporated in YOLO series framework. The proposed method is evaluated on three public datasets, NEU-DET, PCB-DET, and GC10-DET. The mAP of the method is 80.4%, 95.8%, and 76.3%, which are 3.6%, 2.1%, and 3.9% higher than that of YOLOv8 and significantly higher than the state-of-the-art (SOTA) detection methods. Also, the method achieves the second-highest inference speed among the thirteen models tested. The results indicate that DsP-YOLO is expected to be used for real-time defect detection in industry.