SWD: Low-Compute Real-Time Object Detection Architecture

Abstract

AbstractIn recent years, deep learning-based object detection has been researched hot spot due to its powerful learning ability in dealing with occlusion, scale transformation, and background switches, etc. There are many state-of-art object detectors like SSD, SSH, YOLO, and RCNN that have been invented in recent years. These architectures are highly complex, work on deep learning framework, requiring high computing power, which restricts their practical adaptability for low-cost applications and low-computes devices. In this work, a novel real-time object detector architecture, sliding window detector (SWD) based on a sliding window technique, has been proposed. SWD works on a deep learning framework and can execute on a low-compute device. In the proposed SWD architecture, the classifier network is optimized. The fully connected layer of the classifier trained on N classes is replaced by a convolutional layer, which generates N heat-maps. These heat-maps are used to localize and classify the object. SWD simulation on an Intel i5 CPU with 20 FPS shown mAP 0.85 for PKLOT data-set.KeywordsObject detectionSliding windowCar countingSWD