Comparison Between Block-Wise Detection and A Modular Selective Approach

On-road risk detection and alert system is a crucial and important task in our day to day life. Deep Learning approaches have got much attention in solving this noble task. In this paper, we have performed a comparative study on two recent architectures that handle the on-road risk detection task, which are Block-Wise Detection and Modular Selective Network (MSNet). In the Block-Wise Detection, we have used the VGG19, VGG19-BN, and ResNet family as the backbone network. On the other hand, for MS-Net we have used the ResNet-44 as the router and ResNet-101 as the expert network. In this experiment, we evaluate our model on an "on-road risk detection dataset", which was created by our research group using an RGB-D sensor mounted on a senior car. On this dataset, we can achieve an accuracy of 89.40 % for MS-Net. For the Block-Wise Detection model, we can achieve an accuracy of 90.51% if we use ResNet50 as the backbone network. However, if we choose the network models used in MS-Net, we can double the inference speed. Thus, compared with Block-Wise Detection, we think the overall performance of MS-NET is better, and is potentially more useful for driving assistance of elderly drivers.