An overload behavior detection system for engineering transport vehicles based on deep learning
Abstract
This paper builds an overloaded truck detect system called ITMD to help traffic department automatically identify the engineering transport vehicles (commonly known as ‘dirt truck’) in CCTV and determine whether the truck is overloaded or not. We build the ITMD system based on the Single Shot MultiBox Detector (SSD) model. By constructing the image dataset of the truck and adjusting hyper-parameters of the original SSD neural network, we successfully trained a basic network model which the ITMD system depends on. The basic ITMD system achieves 83.01% mAP on classifying overload/non-overload truck, which is a not bad result. Still, some shortcomings of basic ITMD system have been targeted to enhance: it is easy for the ITMD system to misclassify other similar vehicle as truck. In response to this problem, we optimized the basic ITMD system, which effectively reduced basic model’s false recognition rate. The optimized ITMD system achieved 86.18% mAP on the test set, which is better than the 83.01% mAP of the basic ITMD system.
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Image recognition can help law enforcement detect overloading of trucks.
“For example, image recognition can help law enforcement detect overloading of trucks [888].”
From Tackling climate change with machine learning (2023) · cites this paper as [888]
Cites
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
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Tackling Climate Change With Artificial Intelligence
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AI for Tracking Overloaded Commercial Vehicles
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Vehicle Overload Detection System for Goods Train using IoT and Vision-based AI
Weslie, J. A., Vijay Balaji, M., Chandrasekar, V., Theekshitha, B., Karthik, N., & Venkata Ramana Rao, S. (2026) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici69824.2026.11724292
- Authors
- Zhou, L., & Wu, G.
- Affiliations
- Shanghai Jiao Tong University (CN)
- ORCID
- Wu, Gang
- Venue
- AIP Conference Proceedings
- Publisher
- Author(s)
- Published
- 2018
- Volume
- 1955
- Pages
- 040038
- ISSN
- 0094-243X (ISSN-L)
- OpenAlex
- W2802162078
- Cited by
- 8
- References
- 9
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 2 · 2022: 2 · 2018: 1
- FWCI
- 0.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- AIP conference proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Mechanical Engineering › Transport Systems and Technology
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- CCTV surveillance, Single Shot MultiBox Detector, object detection, false positive reduction, traffic law enforcement, image dataset construction
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