Overview of single-stage object detection models: from Yolov1 to Yolov7
Abstract
Recently, with the rise of Artificial Intelligence (AI) and computer vision techniques, object detection using visual automated methods has attracted much attention from computer vision researchers. It is leveraged and integrated in a wide range of real-time applications in view of the reliability of the delivered information. The visual detection relies on processing the appearance and motion features received from Electro-optical (EO) and thermal sensors. Moreover, the detection methods have known a significant evolution in terms of methods used, they moved from using handcrafted feature-based methods to deep learning algorithms with two-shot and single shot object detectors. Real time applications use Single Shot object Detectors (SSD) thanks to the optimal compromise that they present between speed and performance in comparison to their counterpart. You Only Look Once (YOLO) algorithms have demonstrated high performance and fast inference speed in several applications, thus they are extensively exploited and improved properly from the first version. This paper present an exhaustive overview and comparison of Yolo algorithms including the major contributions of each version as well as their evolution with respect parameters used and the followed architecture.
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Single-stage object detectors are particularly well suited for applications that demand quick inference, such as autonomous vehicles and surveillance systems, due to their simplicity, computational efficiency, and real-time processing capabilities.
“This approach has gained popularity due to its simplicity, computational efficiency, and real-time processing capabilities, making it particularly well suited for applications that demand quick inference, such as autonomous vehicles and surveillance systems [17].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [17]
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- Authors
- Yasmine, G., Maha, G., & Hicham, M.
- ORCID
- Yasmine, Ghazlane
- Venue
- 2023 International Wireless Communications and Mobile Computing (IWCMC)
- Publisher
- IEEE
- Published
- 2023-06-19
- Pages
- 1579-1584
- OpenAlex
- W4384946077
- Semantic Scholar
- 3b88ac7439997c3cbf2593dc08818e6dd7352676
- Cited by
- 37
- References
- 38
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 14 · 2023: 3
- FWCI
- 3.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Wireless Communications and Mobile Computing Conference (IWCMC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- one-stage object detection, YOLO, YOLOv7, YOLOv5, YOLOv6, YOLOv4, YOLOv3, real-time object detection, deep learning object detection
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