A comprehensive survey of deep learning-based lightweight object detection models for edge devices
Open accessAbstract
This study concentrates on deep learning-based lightweight object detection models on edge devices. Designing such lightweight object recognition models is more difficult than ever due to the growing demand for accurate, quick, and low-latency models for various edge devices. The most recent deep learning-based lightweight object detection methods are comprehensively described in this work. Information on the lightweight backbone architectures used by these object detectors has been listed. The training and inference processes concerning to deep learning applications on edge devices is being discussed. To raise readers’ awareness of this developing domain, a variety of applications for deep learning-based lightweight object detectors and related utilities have been offered. Designing potent, lightweight object detectors based on deep learning has been suggested as a counter to such problems. On well-known datasets such as MS-COCO and PASCAL-VOC, we thoroughly examine the performance of certain conventional deep learning-based lightweight object detectors.
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Small objects often lack sufficient feature representation in YOLO's grid-based framework, as the resolution of feature maps tends to diminish with increasing model depth.
“Small objects often lack sufficient feature representation in YOLO's grid-based framework, as the resolution of feature maps tends to diminish with increasing model depth [143].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [143]
YOLO has difficulty detecting small objects, particularly in cluttered or complex environments.
“A major challenge is its difficulty in detecting small objects, particularly in cluttered or complex environments.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [143]
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Mittal, P.
- Affiliations
- Thapar Institute of Engineering & Technology (IN)
- Corresponding author
- Mittal, Payal
- ORCID
- Mittal, Payal
- Venue
- Artificial Intelligence Review
- Published
- 2024-08-10
- Volume
- 57
- Issue
- 9
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W4401485877
- Cited by
- 244
- References
- 20
- Citations by year
- 2026: 146 · 2025: 94 · 2024: 4
- FWCI
- 34.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Artificial Intelligence Review (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- lightweight object detection, edge devices, edge computing, MS COCO, Pascal VOC
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