Automated detection and classification of spilled loads on freeways based on improved YOLO network

This study aims to utilize a modified you only look once (YOLO) network to address the detection and classification of spilled loads on freeways. YOLO architecture was augmented in two ways. Firstly, a kernel size of 1 × 1 for the conv layers was used. Secondly, the use of connections between the convolution layers was proposed. For training the network, a synthetic dataset was constructed where ImageNet was used to choose ten types of spilled load objects and KITTI dataset as the background. The objects were blended in the KITTI images' road region, where the road area is segmented through an already trained network previously available. The testing dataset was constructed with manually taken photographs. Experiment results showed that the training model can arrive at an accuracy rate of 74%. The trained model was also demonstrated on the test set generated by taking the background images with camera mounted on a station wagon and on a field test.