DDNet: Deformable Convolution and Dense FPN for Surface Defect Detection in Recycled Books

TL;DR

DDNet is a novel surface defect detection model that combines deformable convolution and dense feature pyramid networks to improve accuracy in identifying diverse defects on recycled books.

Contribution

The paper introduces DDNet, integrating deformable convolution and dense FPN modules, along with a new dataset for enhanced surface defect detection in recycled books.

Findings

Achieved a 46.7% mAP on the new dataset.

Improved defect localization and classification accuracy.

Outperformed baseline models by 14.2% mAP.

Abstract

Recycled and recirculated books, such as ancient texts and reused textbooks, hold significant value in the second-hand goods market, with their worth largely dependent on surface preservation. However, accurately assessing surface defects is challenging due to the wide variations in shape, size, and the often imprecise detection of defects. To address these issues, we propose DDNet, an innovative detection model designed to enhance defect localization and classification. DDNet introduces a surface defect feature extraction module based on a deformable convolution operator (DC) and a densely connected FPN module (DFPN). The DC module dynamically adjusts the convolution grid to better align with object contours, capturing subtle shape variations and improving boundary delineation and prediction accuracy. Meanwhile, DFPN leverages dense skip connections to enhance feature fusion, constructing a hierarchical structure that generates multi-resolution, high-fidelity feature maps, thus effectively detecting defects of various sizes. In addition to the model, we present a comprehensive dataset specifically curated for surface defect detection in recycled and recirculated books. This dataset encompasses a diverse range of defect types, shapes, and sizes, making it ideal for evaluating the robustness and effectiveness of defect detection models. Through extensive evaluations, DDNet achieves precise localization and classification of surface defects, recording a mAP value of 46.7% on our proprietary dataset - an improvement of 14.2% over the baseline model - demonstrating its superior detection capabilities.