The major characteristics of vehicles for hazardous chemicals transportation are the danger sign on the roof and the dangerous goods sign beside the license plate, which are difficult to detect for most object detection algorithms. To improve the detection accuracy and enhance the detection speed, this study proposes a novel detection algorithm for these vehicles based on the residual network (ResNet) and bidirectional feature pyramid network. A data set of vehicles for hazardous chemicals transportation is first made by the interception of the highway surveillance video, and then feature extraction is performed with the ResNet. In this novel model, the recurrent residual module is used to replace the middle convolution layer of the residual block. Then the bidirectional feature pyramid network is employed for feature fusion. Finally, the prediction results are obtained with the prediction network. Performance verification is carried out on the test set, and the results show that the indicators of the proposed model are superior to those of other networks overall. It has the detection accuracy up to 0.961 and the frames per second (FPS) of 43.5, showing a good industrial application prospect.