PERE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes

Laminated panels are widely used in industry, and their quality inspection has traditionally relied on manual labor, which is time-consuming and prone to errors. Automated detection can significantly improve efficiency and reduce human error. With prior knowledge, object detectors focus on updating model structures to improve performance. Despite initial success, most methods become increasingly complex and time-consuming for industrial applications while also neglecting the object distributions in the industrial dataset, especially in the context of industrial laminated panels. All these issues have led to missed and false detections of objects in this scene. We therefore propose a prior-enhanced resolution-extended object detector framework (PERE) for industrial scenarios to solve these issues while enhancing detection accuracy and efficiency. PERE explores the spatial connection of objects and seeks the latent information within the process of forward propagation. PERE introduces the prior-enhanced network (MRPE) and the resolution-extended network (REN) to replace initial modules in one-stage object detectors. MRPE extracts prior knowledge from the spatial distribution of objects in industrial scenes, migrating false detections caused by feature similarities. REN incorporates super-resolution information during the upsampling process to minimize the risk of missing tiny targets. At the same time, we have built a new dataset SPI for studying this topic. Comprehensive experiments show that PERE significantly improves efficiency and performance in object detection within industrial scenes.