# TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5
## 1 Introduction  
With the rapid development of the economy and the continuous improvement of people's pursuit of high-quality life, the demand for automobiles continues to rise, and automobile production also increases [1]. In the context of traffic congestion and frequent accidents, autonomous driving technology [2] has become a major topic of common concern for the government and enterprises. Early in the history of autonomous driving technology, automatic driving systems relied on accurate sensor data, the parameter configuration often needed to be manually set by the developer, and it needed to pass field tests and repeated debugging [3]. Traditional methods have several disadvantages, the main challenge being that manually adjusted parameter configurations are inherently labor-intensive and have limited adaptability to new applications [4]. With the rapid development of artificial intelligence [5], machine learning [6], mobile internet [7] and other technologies, autonomous driving technology has made remarkable progress. As an important branch of artificial intelligence, deep learning [8] can extract useful feature information from driving data by building a complexneural network model, achieve accurate positioning of vehicles, behavior prediction and other functions, greatly improve the accuracy and speed of object detection, and promote the development of automatic driving systems to a more intelligent direction [9]. Object detection is an important part of automatic driving systems. It is designed to identify specific objects in an image or video and determine the location of those objects. However, in practical applications, due to the different appearance and shape of objects, as well as the interference of lighting, occlusion and shooting angle, object detection is still facing an enormous challenge [10]. Traditional object detection methods usually extract features from areas that may contain objects, such as scale-independent feature transform (SIFT) [11], gradient-oriented graph (HOG) [12] and other feature extraction algorithms. However, these methods require a large computational load and have slow operation speeds, and the recognition effect is not good enough with low accuracy. Recent advances in deep learning have revolutionized the field of object detection. By using convolutional neural networks (CNN), the system can now extract features from images in layers, so as to detect key objects such as cars, pedestrians and road signs in real time with extremely high accuracy [13]. These algorithms not only improve the accuracy of recognition, but also significantly reduce the false positives and missed detection rates, providing more reliable environmental perception information for autonomous vehicles. At present, there are two kinds of object detection methods based on deep learning: two-level detection and single-level detection. The typical representative of two-level detection technology is R-CNN series [14]. Firstly, it proposes the region of the image, then extracts and classifies the feature of each proposed region, and finally performs boundary box regression on the classification results to obtain the exact target position. After the classification is complete, the post-processing steps are used to eliminate overlapping or redundant bounding boxes to ensure high precision detection results. In addition, a large amount of computing overhead hinders effective deployment on mobile devices with limited processing power. In contrast, single-stage detection technology takes a more direct approach. After the user inputs an image, the system can directly output the detection result, including the category of the target box, the confidence score and the object in the box. The detection and classification process can be completed by only one network, so it has the advantages of high real-time, fast inference, model parameter reduction, and low computational complexity.