I²GF-Net: Interlayer Information Guidance Feedback Networks for Wood Surface Defect Detection in Complex Texture Backgrounds

Visual surface defect detection is crucial for product quality control in the large-scale wood manufacturing industry. This study focuses on how to assist the deep learning model in surviving the challenges brought by complex texture backgrounds. A novel visual defect detection model, interlayer information guidance feedback networks (I2GF-Net), is proposed in this article. To be specific, a top-down feedback encoder (TDFE) is proposed to guide the attention of the low-level feature map, enabling it to focus on the defect regions by incorporating enhanced high-level semantic information. This significantly reduces false positives triggered by intense textures. Meanwhile, a semantic feature texture enhancement (SFTE) method is designed to compensate for high-level semantic features with fine-grained local information, thereby avoiding frequently missed detections resulted from multiple down-sampling in deep models. Furthermore, we provide an option of dual-round feature refinement (DRFR) to pursue a higher mean average precision (mAP) in scenarios where sacrificing a certain amount of time is acceptable. Experimental results demonstrate the I2GF-Net outperforms 13 state-of-the-arts on two benchmark datasets (VSB-DET and NEU-DET), as well as our newly opened wood dataset (OULU-DET), which will be publicly available at
http://www.ilove-cv.com/oulu-wood/
.