Research on the control of overloading of muck vehicles based on morphological feature extraction from the perspective of deep learning

With the acceleration of urbanization, urban demolition, construction, reconstruction and other engineering projects to promote urban waste transportation activities increasingly frequent, but because of overload, overrun, dangerous driving and other non-standard operation problems, endangering social and traffic safety. Traditional police law enforcement has problems such as management and governance leakage, incomplete coverage, and weak law enforcement. In the application of the existing detection methods based on traditional loadometer weighing and road weighing, there are pain points such as high equipment investment, small detection range, and easy damage of equipment. In order to do a better job of muck vehicle management, this paper breaks the traditional muck vehicle management and loadometer weight measurement method, and creatively puts forward no gap dynamic real-time supervision and machine vision based on deep learning. Through the accurate judgment of overload and limit of muck vehicle morphological detection, it effectively solves the problem of no blind spot, continuous and less investment in law enforcement. It provides an effective management scheme for the accurate law enforcement and violation remediation of traffic control departments and administrative departments.