Outdoor Garbage Detection Based on Improved YOLOv5

Owing to the increasing severity of garbage pollution, automatic garbage detection has become significantly important in practice. The detection mechanism of YOLOv5 is improved in this study to achieve better performance in outdoor garbage detection against a complicated background. Moreover, here, a garbage dataset is constructed comprising six garbage image types collected in a complex background; subsequently, a simple yet efficient method is proposed to generate ground truth heat maps of garbage objects presented in the images. We treat the corresponding heat maps as a quantization standard and then obtain a branch structure based on YOLOv5 by conducting experiments to generate predicted heat maps. Subsequently, the predicted heat maps are sent back to the backbone structure of YOLOv5 to increase the spatial attention weights of the feature maps in the training process to improve the performance of the entire target detection network. Only a few parameters are added to the improved network, which generates proper predicted heat maps and the performance of garbage detection has been greatly improved.