# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 2 Related Works and Methods
### Methods
#### 2.2.2 HRNet Feature Extraction Backbone Network  
The feature extraction backbone network is a crucial component in object detection, responsible for transforming input images into high-dimensional feature vector representations that contain information about different objects in the image. The original feature extraction backbone network in YOLOv5 demonstrates excellent performance in most detection tasks due to its simple structure, low overfitting risk, rapid detection pace, and superior accuracy. However, it tends to overlook the fine details of small objects. Additionally, when detecting small objects in complex power operation scenes, the DarkNet53 structure may not be sufficiently complex and is prone to interference from complex background noise, resulting in false positives or missed detections. When extracting features using the original backbone network in YOLOv5, strong semantic information is obtained through multiple downsampling operations, and the feature maps of different resolutions are connected in series. However, this approach results in a notable reduction in fine-grained information within the low-resolution feature maps of small objects. To mitigate the diminishment of feature details pertaining to small objects in power operation scenes, we designed a backbone network based on HRNet, as illustrated in Figure 2.  
Figure 1: Network structure of the HS-YOLO algorithm. Figure 2: HRNet Feature Extraction Backbone Network. The backbone network of HS-YOLO begins with a high-resolution branch as its initial branch, progressively adding subnets from high to low resolutions. These branches corresponding to distinct resolution feature maps operate concurrently, continuously engaging in feature fusion across different branches. Each high and low-resolution feature benefits from repeated information extraction from other parallel connections, thereby enriching the feature details pertinent to small objects. From the structure diagram, it can be observed that after the input of surveillance images from power operation scenes into the network, multiple-stage structures and transition structures are employed to obtain three sets of feature maps with resolutions of 1/8, 1/16, and 1/32 in relation to the original size. The stage structure is used to extract and compress image features and fuse processed features of different sizes, while the transition structure is used to add new branches of different scales in parallel. The transition structure in the network consists of CBS layers. It includes a series of depthwise separable convolution layers, normalization layers, and activation function layers. Its main function is to add a new scale branch in parallel. Within Transition1, two convolutional layers are utilized in parallel, each with a kernel size of 3 \(\times\) 3. One of these layers operates with a stride of 1, adjusting the channel count to yield a scale branch downsampled by a factor of 8. Meanwhile, the other convolutional layer employs a stride of 2, both modifying the channel count and altering feature dimensions, thus generating scale branches downsampled by a factor of 16. In Transition2, a convolution layer featuring a 3 \(\times\) 3 kernel size and a stride of 2 is added on top of the smallest scale branch to obtain a new scale branch downsampled by a factor of 32. The transition structure controls the resolution and channel numbers to facilitate subsequent feature fusion. The adjustment of channel numbers helps reduce computational complexity and parameter count. In the Stage structure of the backbone network, Stage1 is different from either Stage2 and Stage3. Stage1 includes three CBS modules and one Layer module. The Layer module is composed of stacked Bottleneck modules, enabling channel count adjustment without modifying the feature dimensions. In Stage2 and Stage3, feature extraction and compression are performed using four BasicBlock modules, followed by a FuseLayer fusion module for information interaction across different scale branches.