Deformable Part Region Network for Automated Waste Recycling

Abstract

Automated waste recycling is a critical step toward sustainable waste management, requiring efficient and intelligent sorting systems. Traditional recycling methods rely on rule-based or handcrafted feature extraction approaches, which often struggle with complex waste compositions. The increasing demands of automated waste recycling systems necessitate advancements in object detection technologies, particularly for deformable objects such as waste materials. This work introduces a new deep learning framework, the Deformable Part Region Network (DPR-Net), which excels in detecting and segmenting deformable objects in challenging, unstructured environments such as waste recycling facilities. By integrating deformable convolutional networks with region-based Convolutional Neural Network (CNN) architectures, the DPR-Net dynamically adapts to the geometric variations of irregular waste items, enhancing both detection precision and segmentation accuracy. Our approach leverages the ZeroWaste dataset, a comprehensive dataset tailored for recycling scenarios, to train and validate the model. Results indicate significant improvements in detection metrics over traditional methods, providing a robust solution for automated waste sorting and contributing to environmental sustainability efforts. The presented system shows result with Recall 93.1, Precision 92.5, mAP 85.3, FPS 2.2 and Accuracy 94.2. Extensive experiments on benchmark waste datasets demonstrate that DPRN outperforms existing state-of-the-art methods.