## 1 Introduction  
It has become increasingly common for vehicles to be overloaded, leading to accidents. As a result of overloading, a driver's ability to brake and steer is reduced, which can lead to accidents. With respect to the unnecessary stress on the engine, the chances of tire failure increase at a greater rate, which reduces vehicle stability [1, 2]. Road accidents caused by vehicles transporting goods are on the rise due to overloading. The other major concern is the possibility of casualties that may result from these truckloads exceeding their capacity. This may also cause damage to roads to a greater extent. As per the work mentioned in [1, 2], difficulties in meeting capacity requirements are caused by errors in design and implementation. Overloading also has an effect on the uneven maintenance and repair of roads, as is mentioned in [3, 4, 5].  
In this regard, truck users who load beyond the maximum capacity can cause major concern with a realistic probability of accidents on highways [6]. Analysis has shown that these heavy overloaded trucks are predominantly found near factory areas, leading to these roads breaking down more quickly. In addition, the need for road repairs causes concern every year given their serious effects on road users. From a wider perspective, these situations disrupt the economy of the nation. Deeper analysis shows that overloaded vehicles damage the road and occupy most of the space on the road, especially in rural areas. A major concern for rural areas is that most of the commuting roads in India are single- and double-lane only. In addition to these adverse drawbacks, it is difficult for other drivers to overtake these vehicles. Another problem with vehicles overcrowded with goods is the possibility of disturbing power lines and properties near the road. As we are moving towards the era of smart cities in developing countries, solutions for this problem are to be identified at the preliminary stages using machine learning. Solutions to overcome this problem can be achieved with a deep convolutional neural network for classifying overloaded and non-overloaded vehicles that can be used to detect overloaded vehicles in the absence of human interference. This shows the way to solve this issue with respect to the global standardization of the smart city. In the field of computer vision, deep learning is the most effective algorithm for solving problems, in part due to its capacity for object detection and classification, which exhibit the best solutions for this problem. Thereby, this model allows for a complete check on over-volume vehicles in areas where human supervision may not be possible, such as in remote mountain regions, sub-rural areas, and forest areas. In this way, this model provides a solution for the classification of over-loaded vehicles.  
This work proposes the best performing model for identifying over-volume vehicles applied in the setting of roads and traffic. In this work, we use the ImageNet weights of complex DCNNs as the initial features, perform fine-tuning by unfreezing one-fourth of the portion of the neural network, and train the collected dataset. These approaches helped to generate the features of DCNNs related to our collected dataset and significantly increased the performance of DCNNs by 5-10%.  
The main contributions of this work are as follows:  
* A new real-time dataset consisting of 3054 images categorized into three different classes of over-volume goods carriers, goods carriers that are not over-volume, and vehicles not carrying goods.
* The implementation of various state-of-the-art (SOTA) deep convolutional neural networks for classifying vehicles with the help of transfer learning and fine-tuning.
* A thorough performance analysis of DCNNs on the collected dataset.