eprintid: 16399
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/63/99
datestamp: 2023-12-19 03:22:55
lastmod: 2023-12-19 03:22:55
status_changed: 2023-12-19 03:06:11
type: article
metadata_visibility: show
creators_name: Sumit, S.S.
creators_name: Awang Rambli, D.R.
creators_name: Mirjalili, S.
creators_name: Ejaz, M.M.
creators_name: Miah, M.S.U.
title: ReSTiNet: On Improving the Performance of Tiny-YOLO-Based CNN Architecture for Applications in Human Detection
ispublished: pub
note: cited By 6
abstract: Human detection is a special application of object recognition and is considered one of the greatest challenges in computer vision. It is the starting point of a number of applications, including public safety and security surveillance around the world. Human detection technologies have advanced significantly in recent years due to the rapid development of deep learning techniques. Despite recent advances, we still need to adopt the best network-design practices that enable compact sizes, deep designs, and fast training times while maintaining high accuracies. In this article, we propose ReSTiNet, a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy. Following SqueezeNet, ReSTiNet adopts the fire modules by examining the number of fire modules and their placement within the model to reduce the number of parameters and thus the model size. The residual connections within the fire modules in ReSTiNet are interpolated and finely constructed to improve feature propagation and ensure the largest possible information flow in the model, with the goal of further improving the proposed ReSTiNet in terms of detection speed and accuracy. The proposed algorithm downsizes the previously popular Tiny-YOLO model and improves the following features: (1) faster detection speed; (2) compact model size; (3) solving the overfitting problems; and (4) superior performance than other lightweight models such as MobileNet and SqueezeNet in terms of mAP. The proposed model was trained and tested using MS COCO and Pascal VOC datasets. The resulting ReSTiNet model is 10.7 MB in size (almost five times smaller than Tiny-YOLO), but it achieves an mAP of 63.74 on PASCAL VOC and 27.3 on MS COCO datasets using Tesla k80 GPU. © 2022 by the authors.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138644537&doi=10.3390%2fapp12189331&partnerID=40&md5=1e2f0b810f7b9b292029a19ec27479f3
id_number: 10.3390/app12189331
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 12
number: 18
refereed: TRUE
issn: 20763417
citation:   Sumit, S.S. and Awang Rambli, D.R. and Mirjalili, S. and Ejaz, M.M. and Miah, M.S.U.  (2022) ReSTiNet: On Improving the Performance of Tiny-YOLO-Based CNN Architecture for Applications in Human Detection.  Applied Sciences (Switzerland), 12 (18).   ISSN 20763417