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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Research Article| April 18 2018
An overload behavior detection system for engineering transport vehicles based on deep learning
AIP Conf. Proc. 1955, 040038 (2018)
Libo Zhou, Gang Wu; An overload behavior detection system for engineering transport vehicles based on deep learning. AIP Conf. Proc. 18 April 2018; 1955 (1): 040038. https://doi.org/10.1063/1.5033702
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