# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 2 Related Work
### Research on Target Detection for Transmission Lines  
In the early stages of employing computer vision for detecting foreign objects on transmission lines, traditional digital-image processing techniques were often used, employing non-neural network-based methods to extract defect features from transmission line images [27]. For instance, [PERSON] et al. [28] employed contour detection of the transmission conductor's shape to determine the presence of foreign objects. [PERSON] et al. [29] proposed an Otsu adaptive threshold segmentation algorithm, improved with morphology, to remove background noise, and applied a gradient method to identify power line edge positions, utilizing the Hough transform to analyze the number of lines to achieve foreign object recognition. [PERSON] et al. [30] introduced a detection method for foreign objects on transmission lines based on line structure perception, segmenting and analyzing the grayscale and line width of acquired line segments in horizontal and vertical directions, to identify foreign objects on the transmission lines within complex backgrounds. However, the rich and diverse details in images of foreign objects on transmission lines make it challenging to describe target features using manually designed single features comprehensively. These methods exhibit relatively low accuracy in identifying foreign objects and struggle to differentiate between anomalies. With the rapid advancement of deep learning, methodologies for detecting foreign objects on power transmission lines based on deep learning have been proposed. [PERSON] et al. [31] initially employed the Selective Search method to extract candidate regions from pole images. Subsequently, training was conducted using the CaffeNet network model, adjusting and optimizing samples and network parameters through pre-training and re-training, resulting in a detection accuracy of 92.46% for nests. [PERSON] et al. [32] processed images using the scale histogram-matching method and employed the DFB-NN model, which effectively integrates low-resolution information and high-semantic information, to detect various objects, including vehicle-mounted cranes, tower cranes, forklifts, winding foreign objects, and wildfires, achieving an accuracy of 88.1%. [PERSON] et al. [33] utilized the DenseNet network to replace the penultimate layer network in YOLO v3, achieving average detection accuracies of 94.7% for kites, nests, and trash. [PERSON] et al. [34] used Otsu adaptive threshold segmentation, morphological processing and other methods to extract regions of interest. Then, DenseNet201 was used to extract the features of the regions of interest. Finally, the ECOC-SVM model was trained, and the average accuracy of balloons, kites, plastics and nests reached 93.3%. [PERSON] et al. [35] employed a concatenated fusion strategy to merge features extracted by different networks. They utilized a random-forest classification model to achieve a detection accuracy of 95.8% for balloons, kites, nests, and plastics. [PERSON] et al. [36] replaced the FPN structure of YOLOv5 with the BiFPN structure, integrated the CA attention mechanism into the CSP2_X module, and added a small-object detection layer, resulting in an average detection accuracy of 98.3% for nests, plastics, and kites. [PERSON] et al. [37] proposed the YOLOv4-EDAM model, utilizing the DnCNN image denoising network to reduce image noise and employing SENet-MobileNetV2 to extract features. Additionally, the model enhanced the network's feature extraction capability using CBAM-SPP and CBAM-PANet, and utilized NMS to eliminate overlapping boxes, achieving a detection accuracy of 96.71% for nests, kites, balloons and trash. [PERSON] et al. [38] used the ASPP module in YOLOX, incorporated the CBAM attention mechanism, employed the GIOU loss function, and achieved a detection accuracy of 86.57% for nests, balloons, trash and kites. [PERSON] et al.