## 2 Related Work  
The majority of the existing research focuses on overloaded vehicle detection with the help of sensors and other external devices. In [7], the authors discuss the ground vibrations generated by vehicles with different weights. This work uses an external device called a geophone that helps to measure tiny vibrations in the ground. With the vehicles passing by the road, the geophone can measure the vibrations and transmit the vibration data through the wireless transmission module. In this analysis, a support vector machine algorithm is used on the collected data to detect the overloaded vehicles. The work conducted in [7; 8] prioritizes the practical conditions of the vehicle load control framework through the load cell strain size implemented in the vehicle. The single-chip microcontroller gathers data that are transmitted and obtained through sensors and calculates the overall vehicle load. The work proposed in [9] uses load cells to measure the weights of the goods and introduces an engine locking system in the presence of an overload. The weights measured by load cells are compared to the threshold weights by Arduino HX711, and in the presence of overload the DC motor system's ignition is stopped. Similarly, in this work, the authors introduce a vehicle load control framework with the help of a single-chip microcontroller and weight sensors. In the work proposed in [10], the authors introduce a vehicle load control framework with the help of a single-chip microcontroller and weight sensors. Similarly, in [10], weight sensors are used to determine the entire weight of the load being carried by the vehicle. When the load is too heavy, the microcontroller sends a signal to the vehicle system to prohibit it from starting. Although there are systems that focus on overload vehicle identification, to the best of our knowledge, this is the first study that focuses on classifying over-volume vehicles as a vision-based task. The conventional methods for handling vision-based tasks involve customized feature extractors followed by machine learning classifiers. For example, categorizing the vehicles in images taken from surveillance cameras as mentioned in [11] uses a histogram of oriented gradients (HOG) descriptor for feature extraction and a support vector machine classifier for classification. Here, they divide the vehicles into three main categories: motorcycles, compact cars, and large cars. In the past few years, deep convolutional neural networks have become impeccable at handling vision-based tasks such as image classification, object detection, image segmentation, etc., with the help of its high-performing feature extractors. The study proposed in [12] uses deep convolutional neural networks (DCNNs) to classify vehicles in a natural setting without cropping the region of interest of the vehicles. In [13], DCNNs are used for the classification of vehicles into four different categories including school, bus, police, and ambulance. The proposed method achieved an average precision and recall of 89% and 83%, respectively. The authors of [14] used ResNet50 architecture to perform transfer learning to classify thirteen different Banglades traffic vehicle types. The research proposed a new dataset consisting of 10,440 images categorized into thirteen types of vehicles. Similar studies [15] have proposed various state-of-the-art DCNNs and performed fine-tuning on ImageNet [16] weights after transfer learning. ResNet-152 architecture provided the best performance with an accuracy of 98.77%. The research was performed on a public dataset called 'Poribohon-BD' [17], which consists of a total of 8138 Bangladesi common vehicle images categorized into 15 types. In [18], the authors collected a new dataset of 10,000 images extracted from road surveillance and driving videos. The dataset is categorized into six classes including rickshaw, motorbike, truck, van, bus, and car. The ResNet architecture gave the best performance with an accuracy of 95.14%.