Comparative analysis on Deep Convolution Neural Network models using Pytorch and OpenCV DNN frameworks for identifying optimum fruit detection solution on RISC-V architecture

Abstract

Making computer detect desired object have always been an area of interest for humans. Object detection can be implemented using following stages: feature extraction, object localization followed by identifying object in input image. Most of the present-day object detection work is focused around x86 and ARM architectures. Researchers constantly strive to either identify better object detection architectures, updated models, improved model accuracies or reduce prediction time. In this paper, multiple pre-trained Deep Neural Network (DNN) models such as Region Based Convolutional Neural Network (RCNN), Fast RCNN, Faster RCNN. You Only Look Once (YOLO) V3 and Single Shot Multibox Detector (SSD) are used to identify fruits in given input image on RISC- V architecture. In order to bring uniformity across all DNN models, all these models are pre-trained on COCO datasets. Experimental results have shown that out of various DNN models tested for object recognition, YOLO and SSD-MobileNet gives optimum performance in terms of accuracy and inference time on RISC- V architecture.