# PBA-YOLOv7: An Object Detection Method Based on an Improved YOLOv7 Network
The SSD algorithm is another deep learning-based target detection algorithm whose core idea is to transform the target detection into a single forward pass regression problem--being able to accomplish both target localization and classification in a single forward propagation. The SSD algorithm performs target detection by applying a series of predefined anchor boxes on feature maps at different scales, sliding these anchor boxes on the feature map, and using convolutional operations to classify and bounding box regression on them to obtain the final target detection results. YOLO is a commonly used single-stage target detection algorithm with fast speed and high accuracy [16; 17]. Its core idea is to transform target detection into a single forward-pass regression problem. A comparison table of the YOLO series versions is shown in Table 1. YOLOv1 [16] is the initial version of the YOLO series. It implements real-time target detection by dividing the input image into grid cells and predicting each cell's bounding box and category probabilities.YOLOv1 uses convolutional neural networks for feature extraction and a fully connected layer for target prediction.YOLOv2 [18] introduces several significant improvements over YOLOv1, including using the Darknet-19 architecture, multiscale training and testing, anchor frames, scale clustering, and fine-grained features.YOLOv3 [19] added more improvements to YOLOv2, including using a Residual Network (ResNet) as a feature extractor, adopting multiscale prediction, and using a feature pyramid network for features of different scales.YOLOv4 [20] introduced some critical improvements in the YOLOv3, introduced CSPDarknet53 as a feature extractor, used the SPP structure, applied multiscale inference, and the PAnet network. YOLOv5 makes some modifications and optimizations based on YOLOv4, including using a lightweight net work structure, introducing model distillation techniques, and adding data enhancement strategies. YOLOX, like YOLOv5, uses the Focus network structure in the backbone part, obtains four independent feature layers, and then makes the four independent feature layers stacked. At this time, the width and height information is concentrated on the channel information, and the input channel is expanded four times [21]. The YOLOv8 model in adaptive multi-sample matching, concerning the YOLOX, adopts the Anchor-Free method and introduces a dynamic TaskAlignedAssigner matching strategy. The YOLO detection algorithm performs satisfactorily in detecting small and occluded targets in complex field environments and has better detection speed than other deep learning algorithms [22; 23]. The single-stage detection algorithm deals with detection as a regression problem, compared to the two-stage detection algorithm that does not require a candidate region and can directly generate information such as category probabilities for the target. Although the single-stage detection method is faster in detection, the accuracy is relatively low. In deep learning network training, the more sufficient samples of the model, the stronger the generalization and the higher the robustness of the trained network model. However, for simple datasets, overfitting, weak generalization ability, and low robustness tend to occur when training the model due to too few samples. [PERSON] presents an inpainting strategy called comparative sample augmentation, which enhances the quality of the training set by filtering irrelevant images and constructing additional images using information about the surrounding regions of the target image. This strategy managed to augment the datasets [24]. The datasets augmented by this strategy significantly reduce the probability of model overfitting during model training and effectively improve the model's generalization ability. While acquiring the dataset, all the image data inevitably have noise, such as granular speckles and discoloration. [PERSON] proposes a hybrid denoising CNN (HDCNN) [25].