# Engineering Vehicle Detection Based on Improved YOLOv6
## 2 Related Work  
Engineering vehicle detection refers to identifying these vehicles within a given area, pinpointing their locations, and accurately classifying their types. Traditional object detection algorithms mainly rely on manually extracted features. For instance, [PERSON] et al. developed a fast and efficient face detection framework based on integral images. They extracted Haar-like features by computing the pixel intensity differences within an image window and used adaptive boosting to enhance the detection process [8]. [PERSON] et al. divided the image window into small spatial regions and then extracted the gradient information [9]. By statistically analyzing the direction and magnitude of gradients in the image to capture local features, they obtained a histogram of oriented gradients (HOG) features. The processed HOG features were then input into a support vector machine (SVM) to effectively recognize pedestrian objects [10]. [PERSON] et al. advanced this by leveraging enhanced HOG features and a pictorial structure model with Latent SVM, effectively detecting deformed objects. This approach, complemented by non-maximum suppression, proved effective in detecting deformed objects with varying attitudes [11]. While traditional object detection algorithms yielded satisfactory results at the time, many of these methods involved multiple steps and demanded substantial manual effort, highlighting the need for improvements in efficiency and accuracy. The rise of deep learning, propelled by advancements in computer technology and computational power, has significantly outperformed traditional algorithms in object detection. Generally, it can be divided into the one-stage method and the two-stage method. The algorithm of the two-stage method typically involves two stages: region proposal, and object classification and location regression. The first stage often employs a CNN to produce a refined region proposal. In the second stage, leveraging the selected region proposal, the algorithm performs object classification and regression, ultimately yielding the object classes and precise coordinates. The prominent two-stage networks encompass R-CNN and its improved algorithms [12]. R-CNN is a pioneering object detection algorithm that extracts candidate regions through selective search and then uses a CNN to perform feature extraction and classification for each region [12]. Fast R-CNN is an improvement over R-CNN, it runs the CNN once on the entire image and then extracts features for each candidate region, which improves detection speed [13]. Faster R-CNN introduces a region proposal network that works in parallel with the CNN, achieving an end-to-end object detection process [14]. Cascade R-CNN is a cascade architecture of R-CNN that progressively improves detection accuracy through multiple stages of detection and refinement, particularly excelling at handling objects of different sizes [15]. R-FCN directly performs feature extraction and classification on candidate regions using a fully convolutional network, eliminating the need for deep feature extraction during the region proposal process, thus improving efficiency [16]. The one-stage algorithm performs the task of bounding box estimation and object classification in a single operation. The YOLO series [5; 17; 18; 19; 20; 21] and single shot multibox detector (SSD) [22] are widely recognized as standard one-stage algorithms. YOLO is a single-stage detection algorithm known for its fast detection speed. It directly predicts bounding boxes and class probabilities on the image without the need for a region proposal. SSD is also a single-stage detection algorithm that detects objects of various sizes by using default boxes on feature maps at different scales. Compared to two-stage algorithms, one-stage algorithms typically exhibit better real-time performance.