Enhanced neuromorphic visual processing by segmented neuron for intelligent vehicle

Abstract

The neuromorphic visual processing framework mimicking the biological vision system offers an alternative process into applying computer vision in everyday environment. With the growing interest for an effective approach for making detection of vulnerable road users for the purpose of safety enhancement, the proposed neuromorphic visual processing was tested on vulnerable road users such as cyclists on the road. The effectiveness of proposed neuromorphic networks of visual processing is evaluated for the vulnerable road user detection technology via maintaining the successful detection rate of over 95% without complex denoising network. The segmented neuron mixed with the rectifier enhanced the performance via extending the detection range by 33 % as well as saving the denoising process. The post enhancement with deep networks becomes flexible that further applications could be sought from incorporating neuromorphic visual processing. The early implementation demonstrated the advantages of fast and robust neuromorphic vision with either the mobile embedded systems of GPU or FPGA hardware processing, or the portable computer based emulator.