# TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5
## 2 Related Work  
So far, the YOLO series has been updated in many versions, with YOLOv5 and YOLOv8 receiving much attention. The YOLOv5 model demonstrates exceptional performance in object detection tasks, achieving a balance between detection speed and accuracy. YOLOv5 has a relatively small model size, making it convenient for deployment on mobile devices and embedded systems. However, although YOLOv8 has further improved detection accuracy, its parameter count has also increased. The computational complexity of YOLOv8 creates deployment challenges for resource-limited edge devices. In contrast, the YOLOv5 has generated considerable research interest, with many researchers exploring architectural improvements and optimization strategies tailored for efficient inference in applications. [PERSON] [22] et al. integrated a feature fusion layer into YOLOv5, along with an attention mechanism, which aims to enhance feature discriminability by focusing on target-related information. However, the weights of channels and spaces need to be calculated separately, significantly increasing the computational load and being unfriendly to the real-time deployment of edge devices. [PERSON] [1] et al. used the deformable convolutional network to optimize the backbone network of YOLOv5, and introduced the DIOU loss function, which effectively improved the detection accuracy and accelerated the convergence speed. Deformable convolution relies on offset learning. If the prediction is inaccurate, it will cause the detection box to shift. Ref. [23] introduced Ghost convolution in the neck C3 module of YOLOv5s, using standard convolution to obtain part of the feature map, and then generating more feature maps through linear operations and connecting them in specific dimensions to extract richer features with a small number of parameters and calculations, thus ensuring detection accuracy and achieving model lightweight. Ghost convolution requires specific hardware support and may not achieve the ideal inference speed on other devices. [PERSON] [24] et al. improved YOLOv8 by replacing the C2f module in the backbone network with a VanillaBlock module, aiming to quantize the model and reduce the parameter count, thereby achieving faster and real-time detection. [PERSON] [25] et al. incorporated the DWR module into YOLOv8 to enhance the multi-scale feature extraction capability of the backbone network, resulting in improved detection accuracy but increased computational load. However, such improvements have led to a significant decrease in detection accuracy. In the field of traffic target recognition, [PERSON] et al. [26] proposed the MCS-YOLO algorithm, adding the swin-Transformer and coordinate attention module to the backbone network to improve the feature extraction performance of the model. However, the swin-Transformer has a high computational complexity and will affect the real-time detection performance. [PERSON] et al. [27] combined NWD with CIoU to improve the sensitivity to the position deviation of small objects. However, NWD's excessive focus on small targets may lead to insufficient feature extraction of large targets, resulting in a decrease in the accuracy of large targets. [PERSON] et al. [28] proposed a light vehicle detection network based on the improved YOLOv5. By combining the detection head with ASFF, the multi-scale feature fusion has been improved. ASFF will increase the frequency of memory access, resulting in a decrease in the actual computing efficiency. Starting from the redundancy of information, [PERSON] [29] et al. chose the FPGM algorithm to prune YOLOv5 to obtain a balance between recognition accuracy and detection speed. TensorRT inference accelerator further improves performance, achieving respectable frame rates on devices such as the Nvidia Jetson Nano and Xavier NX [30]. The above-mentioned studies have made significant progress in object detection in the field of autonomous driving.