# TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5
## 1 Introduction
Although in the initial stage, the accuracy of single-stage detection technology is slightly lower than that of the two-stage method, with the continuous advancement of research, some advanced single-stage algorithms have successfully achieved exceptional accuracy while maintaining high speed and low parameters, making single-stage detection a prominent focus area in contemporary research. In 2015, [PERSON] et al. proposed the You Only Look Once (YOLO) algorithm [15] for the first time, which adopted the architecture of full convolutional neural network to transform the object detection problem into a regression problem, and directly predicted the category and position of the object from the entire image. Although YOLO has improved greatly in speed and accuracy, it still performs poorly in small object detection and complex scenes. Afterward, [PERSON] [16] et al. proposed YOLOv2 and YOLOv3 on the basis of YOLO, introduced multi-scale prediction and a deeper network structure, improved the detection ability of small objects, while maintaining a high detection speed, and effectively enhanced the detection accuracy of multiple objects in complex environments. Although YOLOv3 improves the detection of small objects, it still has limitations when dealing with extreme scenes and more occluders. Additionally, the inference performance of YOLOv3 is still limited by computing resources, especially in the case of high-resolution image input, which requires a large amount of computational power. [PERSON] [17] et al. improved YOLOv3 and proposed YOLOv4 by introducing CSPDarknet53 network, Mish activation function, and data enhancement strategy, etc., which enabled the model to significantly improve detection accuracy while ensuring high speeds, especially in the application scenarios of highways and urban roads. YOLOv4 has improved both in speed and accuracy, but the processing speed depends on the computing resources, especially in the application of edge devices. YOLOv5 [18] uses a more flexible structure to optimize the speed and deployment efficiency of the model while maintaining high accuracy. It has carried out some targeted optimization for the application scenarios of automatic driving systems, especially in the aspect of real-time object detection. YOLOv7 [19] and YOLOv8 [20] have further improved their backbone networks based on their predecessors by introducing more complex modules and connection methods to enhance feature extraction capabilities. With refined loss functions, they have achieved higher detection performance. YOLOv10 [21] integrates an attention mechanism, which further enhances detection accuracy by highlighting key regions and suppressing background noise. To sum up, single-stage detection technology demonstrates significant promise for autonomous driving applications, enabling real-time environmental analysis for vehicles with precise situational awareness capabilities. In addition, with the continuous advancement of technology and the continuous optimization of algorithms, single-stage detection technology can also be used in more complex scenarios and higher-level autonomous driving systems.