# Engineering Vehicle Detection Based on Improved YOLOv6
## 1 Introduction  
Engineering vehicles are heavy machinery dedicated to performing construction engineering tasks on the site, and take a proactive part in supporting the smooth progress of various construction projects [1]. Amidst the rapid growth of both the construction and transportation sectors, the requirement for different types of engineering vehicles is increasing, and problems with the management of engineering vehicles have emerged [2]. Compared with ordinary civilian vehicles, engineering vehicles have the characteristics of having a large cargo load, driving blind spots, large emissions, and an overly wide body, which seriously affect the maintenance of roads and their facilities, as well as traffic safety [3]. To ensure the safe operation of engineering vehicles in urban environments, relevant authorities have issued regulations that impose strict controls on the time, speed, and routes these vehicles may use within cities. This is particularly important for monitoring restricted or time-limited zones, such as city center roads and elevated highways. Nonetheless, compliance issues arise as some engineering vehicles bypass these regulations through unauthorized detours or nocturnal operations, compromising the safety of other road users and inflating traffic supervision costs [4]. This misconduct introduces numerous road traffic safety concerns. Streamlining with advancements in computer technology and artificial intelligence, vehicle detection technologies have been integrated into intelligent transportation systems. These technologies, through sophisticated algorithms and camera systems, enable precise identification and tracking of engineering vehicles. Such measures significantly mitigate traffic management expenses and bolster the safety of pedestrians and other vehicles. As the foundation of an engineering vehicle monitoring system, accurate detection technology is crucial for ensuring the effectiveness of the entire monitoring process. Therefore, developing an algorithm capable of achieving high-precision detection has become an urgent need in current research. To address the challenges of detecting engineering vehicles, this study proposes an innovative detection model that integrates the efficient object detection capabilities of YOLOv6 [5], the deep feature extraction power of a Swin Transformer [6], and the advantages of self-supervised learning offered by a simple framework for masked image modeling (SimMIM) [7]. YOLOv6 is a leading object detection algorithm, distinguished by its quick and reliable detection results. As a member of the sixth generation of the YOLO series, it is crafted specifically for tasks requiring immediate object detection [5]. The Swin Transformer, a vision model based on the Transformer architecture, effectively captures both comprehensive and detailed features through its hierarchical feature representation and self-attention mechanism, showing significant potential in the field of visual recognition [6]. SimMIM, a novel self-supervised learning framework, achieves feature learning without labeled data by masking image regions and predicting their content, providing a solution for data-scar scenarios [7]. By combining these technologies, the main achievements of this study are as follows:  
* Within this research, we combine a Swin Transformer with YOLOv6, specifically optimized for the task of detecting engineering vehicles. The incorporation of the Swin Transformer, with its exceptional global feature extraction and interpretation capabilities, notably boosts the model's precision in recognizing partially occluded vehicles. This technological integration not only represents a major breakthrough compared to traditional convolutional neural network (CNN) methods but also demonstrates higher detection precision and robustness in diverse operational environments. * Given the economic challenges associated with data collection and annotation in the field of engineering vehicle detection, we adopt the SimMIM self-supervised learning paradigm to address this issue.