Cited by 101Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsDomain Adaptation and Few-Shot Learning

YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications

Kumar B., Chethan, Punitha, R., Mohana·2020·2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT)

Abstract

Object detection algorithm such as You Only Look Once (YOLOv3 and YOLOv4) is implemented for traffic and surveillance applications. A neural network consists of input with minimum one hidden and output layer. Multiple object dataset (KITTI image and video), which consists of classes of images such as Car, truck, person, and two-wheeler captured during RGB and grayscale images. The dataset is composed (image and video) of varying illumination. YOLO model variants such as YOLOv3 is implemented for image and YOLOv4 for video dataset. Obtained results show that the algorithm effectively detects the objects approximately with an accuracy of 98% for image dataset and 99% for video dataset.

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Crossref · 10.1109/icssit48917.2020.9214094

Cites

16 of 16 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Since its inception, the YOLO architecture has undergone continuous evolution, with each version from YOLOv1 through to the latest YOLOv9 introducing enhancements in accuracy, speed, and efficiency.

    “Since its inception, the YOLO architecture has undergone continuous evolution, with each version from YOLOv1 through to the latest YOLOv9 introducing enhancements in accuracy, speed, and efficiency [37].”
  • Each YOLO iteration features optimized backbones that contribute to faster processing times and better detection capabilities.

    “Additionally, each YOLO iteration features optimized backbones that contribute to faster processing times and better detection capabilities.”
  • Significant advancements in YOLO include the introduction of anchor boxes, which improve the model's ability to detect objects of varying shapes and sizes, as well as new loss functions aimed at optimizing performance.

    “Significant advancements include the introduction of anchor boxes, which improve the model's ability to detect objects of varying shapes and sizes, as well as new loss functions aimed at optimizing performance.”

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Authors
Kumar B., C., Punitha, R., & Mohana
Affiliations
R.V. College of Engineering (IN)
ORCID
Mohana
Venue
2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT)
Publisher
IEEE
Published
2020-08
Pages
1316-1321
OpenAlex
W3092269081
Cited by
101
References
16
Citations by year
2026: 2 · 2025: 8 · 2024: 21 · 2023: 21 · 2022: 26 · 2021: 23
FWCI
5.40
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Smart Systems and Inventive Technology (ICSSIT) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
YOLOv3, YOLOv4, object detection, multiple object detection, video surveillance, traffic monitoring, KITTI dataset, car detection, human detection, grayscale images, illumination variation

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