# Bird Detection on Power Transmission Lines Based on Improved YOLOv7
## 1 Introduction
### Related Work  
In recent times, there has been a growing interest among researchers in machine vision-based object detection technologies. Currently, object identification and localization algorithms could be broadly divided into two main categories. The initial group, as demonstrated by Faster-RCNN, falls under the label of a \"two-stage algorithm\" [10]. The second category, represented by SSD and the YOLO series, is referred to as the one-stage detection algorithm [11; 12]. Within the two-stage methodology, the fundamental idea is tofirst identify potential regions in the input image and then process these regions through network-based classification and regression. These algorithms offer the advantage of high accuracy, but their drawback is the slower detection speed due to increased computational complexity. In contrast, one-stage detection algorithms dispense with the need for candidate region extraction and instead directly perform regression predictions on each feature map of the input image. However, due to the following reasons, they still struggle to detect small objects. Taking YOLOv7 as an example, when extracting features using CSPDarknet-53 as the backbone network [13], the final feature map has a relatively small size (approximately 1/32 of the input size), resulting in low resolution and a larger receptive field for each pixel in the feature map. This coarseness leads to a poor localization ability for small objects. For instance, a small object may have a size of 32 \(\times\) 32 pixels when input to the network, but it is represented by only 1 \(\times\) 1 pixels in the feature map, posing significant challenges in localization. As a result, common optimization methods for small object detection involve using high-resolution images or feature maps. However, both of these approaches come with expensive computational requirements. Furthermore, the challenges in image acquisition facilities make it challenging to obtain high-definition images of birds in close proximity to power transmission lines.  
In a study presented in reference [14], a bird detection algorithm based on YOLOv3 for power transmission lines was introduced. To enhance the algorithm's capability to detect small objects, a feature fusion detection layer was established by upsampling the 52 \(\times\) 52 scale feature map by a factor of two and combining it with the output of the second residual block. Additionally, by refining the detection confidence scores of the bounding boxes with associated scale factors, optimizations were made to the non-maximum suppression (NMS) algorithm, resulting in an overall enhancement of the model's capability to detect partially concealed birds. However, the algorithm's performance in identifying small and densely clustered targets still remained suboptimal.  
A bird detection method in natural scenes was proposed in [15]. Image data were acquired through the transmission line monitoring device. Residual modules were employed to extract deep-level features from the images. A multi-scale object detection strategy was utilized to ensure effective bird detection. However, its efficacy diminished significantly in the presence of substantial background interference.