# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 2 Related Work  
### Object-Detection Model  
In 2014, [PERSON] et al. [8] used the \"region proposal + convolutional neural network\" to replace the traditional method of \"sliding window + manually designed features\" in object detection, and designed the R-CNN framework, which made a huge breakthrough in object-detection technology. In 2015, [PERSON] et al. [9] proposed the Fast RCNN algorithm, which further improved RCNN and innovatively proposed multi task loss. They also trained classifiers and bounding box regressors, achieving end-to-end training in the detection stage, greatly improving accuracy and speed. [PERSON] et al. [10] proposed the Faster RCNN algorithm shortly after, introducing an RPN network, making candidate box generation almost cost free. In 2016, [PERSON] et al. [11] proposed the YOLO algorithm, which is the first single-stage object-detection algorithm in the era of deep learning, and its speed is very fast. This network divides the image into grids and predicts the bounding box and classification probability of each grid area. A single neural network can obtain results from the complete image through one operation, which is conducive to end-to-end optimization of detection performance. In the same year, combining the anchor mechanism of RCNN and the regression idea of YOLO, [PERSON] et al. [12] proposed the SSD algorithm, introducing multi-scale detection methods, and performing detection on feature maps extracted at each scale. [PERSON] et al. [13] proposed RetinaNet in 2017 to investigate the accuracy of single-stage detection methods that lag behind two-stage detection methods. They believe that the imbalance of categories during the training process leads to a disadvantage in the accuracy of single-stage methods. Therefore, they propose Focal Loss to replace traditional cross entropy, improve the weight of background samples, and make the model more inclined towards target samples that are difficult to detect during the training process. In 2017 and 2018, [PERSON] et al. launched subsequent versions of YOLO: YOLOv2 [14] and YOLOv3 [15]. The YOLOv2 algorithm introduces BN (batch normalization), multi-scale training, anchor box mechanism, and fine-grained features to improve the YOLOv1 algorithm. On the basis of YOLOv2, the YOLOv3 algorithm adopts better backbone network, multi-scale prediction, and nine anchor boxes for detection, which improves the accuracy of the detection algorithm while ensuring real-time performance. In 2020, [PERSON] et al. [16] proposed the YOLOv4 model. In the same year, [PERSON] et al. [17] proposed YOLOv5, which greatly reduced the number of parameters in the YOLO model,but its accuracy was comparable to YOLOv4. In 2021, [PERSON] et al. [18] proposed the YOLOX model. In the same year, [PERSON] et al. [19] proposed YOLOR. In 2022, [PERSON] [20] proposed YOLOv6, which was inspired by the design concept of hardware-aware neural networks. Based on the RepVGG style [21], it designed the reconfigurable and more efficient backbone networks, EfficientRep Backbone and Rep-PAN Neck; it optimized and designed a more concise and effective Efficient Decoupled Head, which further reduces the additional delay overhead caused by general decoupling heads while maintaining accuracy; and the anchor-free mode was adopted, supplemented by the SimOTA [22] label allocation strategy and SloU [23] boundary box regression loss, to further improve detection accuracy. In the same year, [PERSON], C, et al. [24] proposed YOLOv7, proposing different network models for different devices. The concept of a gradient propagation path was used to analyze the reparameterization strategies applicable to different layers of networks, and a planned model structure reparameterization was proposed; the author proposes a new label allocation method called the Coarse to Fine (Coarse to Fine) guided label allocation strategy, which allows Aux Head to generate more grids as positive samples and reduce information loss.