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Buses and heavy vehicles have more blind spots compared to cars and otherroad vehicles due to their large sizes. Therefore, accidents caused by theseheavy vehicles are more fatal and result in severe injuries to other roadusers. These possible blind-spot collisions can be identified early usingvision-based object detection approaches. Yet, the existing state-of-the-artvision-based object detection models rely heavily on a single featuredescriptor for making decisions. In this research, the design of twoconvolutional neural networks (CNNs) based on high-level feature descriptorsand their integration with faster R-CNN is proposed to detect blind-spotcollisions for heavy vehicles. Moreover, a fusion approach is proposed tointegrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) forextracting high level features for blind-spot vehicle detection. The fusion offeatures significantly improves the performance of faster R-CNN andoutperformed the existing state-of-the-art methods. Both approaches arevalidated on a self-recorded blind-spot vehicle detection dataset for buses andan online LISA dataset for vehicle detection. For both proposed approaches, afalse detection rate (FDR) of 3.05% and 3.49% are obtained for the selfrecorded dataset, making these approaches suitable for real time applications.

Blind-Spot Collision Detection System for Commercial Vehicles Using Multi Deep CNN Architecture