# PBA-YOLOv7: An Object Detection Method Based on an Improved YOLOv7 Network
HDCNN improves the quality of the image and makes the image more straightforward and more prosperous in detail. In many image processing and computer vision tasks, such as image recognition, target detection, etc., removing noise improves the accuracy and performance of the algorithm. Reducing noise reduces false detections and misjudgments and improves the algorithm's ability to understand and analyze the image. [PERSON] presented a technique called CBIR-similarity measure via artificial neural network interpolation (CBIR-SMANN) [26]. CBIR-SMANN measures the similarity between an interpolated image and a target image by using an artificial neural network and then calculating the similarity between the interpolated image and the target image. The image can be interpolated from low resolution to high resolution, thus improving the quality and detail of the image. This interpolation technique can be used to generate a high-resolution image similar to the target image by learning the features of the image through a neural network and applying them to the target image. This ultimately improves the accuracy and performance of the algorithm in image recognition or target detection tasks. The application of intelligent vehicles is to be realized in complex urban environments. The target detection technology in the field of automatic driving environment perception not only requires high-precision recognition ability but also needs to be able to respond in real-time to the complex and changing traffic scene to ensure that the driving system can make timely and accurate instructions. Although the two-stage detection algorithm \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Size** & **Train** & **[EMAIL_ADDRESS]** & **FPS** \\ \hline YOLOv1 & 416 \(\times\)416 & VOC2007+2012 & 63.4\% & 45 \\ YOLOv2 & 416 \(\times\) 416 & VOC2007+2012 & 76.8\% & 67 \\ YOLOv3 & 416 \(\times\) 416 & MS COCO & 55.3\% & 35 \\ YOLOv4 & 608 \(\times\) 608 & MS COCO & 65.7\% & 62 \\ YOLOv5-l & 640 \(\times\) 640 & MS COCO & 66.9\% & 73 \\ YOLOv-l & 640 \(\times\) 640 & MS COCO & 68.5\% & 69 \\ YOLOv7 & 640 \(\times\) 640 & MS COCO & 69.7\% & 161 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: YOLO Series Version Comparison Table. has high accuracy, its high time cost leads to slow detection speed which cannot meet the real-time requirements. The single-stage detection method is faster, but the accuracy could be higher, and security needs to be improved. Synthesizing the above network model problems, this paper proposes the PBA-YOLOv7 network model algorithm based on the YOLOv7 model, and the main contributions are as follows:  
1. Firstly, lightweight convolutional Partial Convolution (PConv)is introduced to optimize the ELAN module in the backbone to reduce the number of parameters and the number of visits in the network. 2. Introduce BiFusionNet network to replace the original PANet network of YOLOv7 in the feature fusion module of Neck to enhance the localization signal without increasing the computational burden. 3. Embedding the coordinate attention mechanism module before the representative convolution module in the Neck network to improve the detection accuracy of the network model while ensuring that the network is sufficiently lightweight. The structure of this paper consists of four main parts. The first part details the key issues and challenges of target detection, mainly as applied to intelligent vehicle environment sensing detection, and methods to address these issues. The second part describes the structural components of YOLOv7, the structure of PBA-YOLOv7, and other principles and proposes in detail the improvement methods for each module. The third part is the experimental analysis, which analyzes the detection performance of each improved module in detail, evaluates and analyzes the experimental results, and compares them with similar methods. Finally, the fourth part summarizes the thesis and gives an outlook on the future research direction.