An Intelligent CMOS Image Sensor System Using Edge Information for Image Classification

CMOS image sensors have drawn a lot of attention due to their superior performance in the past decade. The most demanding applications such as visual surveillance and intrusion detection in surveillance systems, and aerial monitoring in conflict zones, are made possible by recent technological advancements in near sensor-based systems. In the existing methodology, image sensors are being used along with a DSP processor to perform image classification and recognition. Continuously reading and transferring data of all pixels to CNN required high computation and power consumption of ADC. In this work, we propose a novel design for an intelligent CMOS image sensor wherein an analog Sobel edge detector is introduced before the ADC. Using the edge detector, instead of digitizing a natural image obtained off the sensor, only edges in the image are digitized and transferred for object classification and recognition tasks. This significantly reduces the power consumed by ADC as only edge-detected pixels are being converted into the digital domain. Analysis shows that object classification tasks on edge-detected images with reduced pixel information from thresholding operation result in pixel reduction by 67 %, 79 %, and 87 % for threshold values of 0.2, 0.3, and 0.4. This work shows that CNN models can still be trained with acceptable accuracy on state-of-the-art models and can reduce operating power for Intelligent edge devices.