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.

这项研究的目的是利用经过修改的仅看一次（YOLO）网络来解决高速公路上溢流荷载的检测和分类。YOLO体系结构通过两种方式进行了增强。首先，使用conv层的内核大小为1×1。其次，提出了卷积层之间连接的使用。为了训练网络，构建了一个综合数据集，其中使用ImageNet选择了十种类型的溢出载荷对象，并以KITTI数据集为背景。将对象混合在KITTI图像的道路区域中，在该区域中，道路区域通过以前可用的已经训练有素的网络进行分割。测试数据集由手动拍摄的照片构成。实验结果表明，该训练模型的准确率可达74％。