Garbage object detection method based on improved Faster R-CNN
Weiting Feng, Wei Yan, Jianbin Xie · 2022
Garbage sorting plays an important and far-reaching role in environmental protection and resource regeneration. At present, garbage sorting is mainly manual assistance. Therefore, because of the characteristics of different object sizes in garbage images, this paper proposes a garbage object detection method based on improved Faster R-CNN. ResNet50 is the backbone network, and a Feature Pyramid Network structure is added to the model. Modified RPN structure parameters, and the original ROI pooling layer is changed to ROI align layer to achieve effective extraction of image features. The experiment shows that the improved Faster R-CNN method with ROI Align and FPN structure is effective in garbage object detection. ROI Align increased by 0.8 percentage points and FPN structure increased by 0.96 percentage points. Finally, the improved Faster R-CNN method achieves 92.81% accuracy on the garbage target detection dataset.