Real-Time Highway Litter Detection Based on Attention Bidirectional Feature Pyramid Cascade Network

As the traffic on highways becomes increasingly busy, littering has become a common traffic safety issue. In this study, we propose a method for highway litter detection based on object detection technology, aiming to achieve real-time and accurate detection and analysis of litter on highways. We proposed an object detection model, which is named ABiFPCN (Attention Bidirectional Feature Pyramid Cascade Network) which is based on YOLO V5 network. Firstly, we add attention mechanism into the model and incorporate a bidirectional feature pyramid network structure to YOLO V5. Secondly, we add multi-scale cascade layer to reduce over-fitting due to exponentially vanishing positive samples. Thirdly, we propose a pooling method based on weighted aggregation to improve the performance of pooling layers in convolutional neural networks when dealing with small targets in single-background scenarios such as highway littering. Finally, we validate the model on public object detection dataset and real-world highway cameras. Experimental results demonstrate that the proposed method achieves high accuracy and reliability in highway litter detection, providing effective monitoring and warning capabilities for traffic management departments, thereby enhancing the level of highway traffic safety.