# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 1 Introduction
However, these methods are specifically designed for small object detection from an aerial perspective and may not be well-suited for the complex variations in small object features and background differences from a monitoring perspective. Detecting small objects poses a challenging task within the domain of computer vision [10]. Existing object detection algorithm frameworks and loss functions are predominantly designed for large and medium-sized objects, leaving minimal focus on small objects. Most network detectors rely on anchor boxes, but fixed anchor sizes may inadequately match the scales of small objects, leading to imprecise localization and the omission of fine details. The loss functions designed for general objects might overlook the scale of small objects during loss computation. Loss functions that lack sensitivity to small objects are unable to accurately locate and classify these objects, resulting in a decline in detection performance. Small object detection in complex power operation monitoring scenes poses even greater challenges, mainly due to the following reasons:  
* The information expressed by the features of small objects in power operation scenes is limited. The limited pixel count of small objects in power operation monitoring images makes it challenging to precisely determine their boundaries and positions. This necessitates the utilization of more intricate detection algorithms to capture discriminative feature information for small object detection [11]. * The object scale spans a large range. In power operation scenes, the scale of objects may vary significantly due to changes in camera angles. This variation hampers the localization accuracy and feature extraction capabilities of detection algorithms. Moreover, different scales of objects require the detector to adjust the search regions, impacting detection speed and reducing real-time performance [12]. * The background of small objects is complex. In the electric power operation scenes, the environment typically comprises various intricate structures and facilities, including different equipment, towers, power lines, and trees, among others. The visual appear ance and textural features of these objects bear a resemblance to small objects, and mutual occlusions may also occur among them. As a result, the complex environment of power operation scenarios tends to perturb the feature information related to small objects, making it more challenging to extract precise details and characteristics [13]. Based on the above-mentioned difficulties in detecting small objects in general scenarios and special power operation scenarios, this paper proposes a detection network called HS-YOLO for practical application needs, which combines a HRNet high-resolution network with sub-pixel convolution. This algorithm enhances the feature extraction for small objects in power operation scenes, capturing more detailed information and effectively addressing the issue of feature loss in small objects, and provides a more reliable guarantee for safe power operation. The primary contributions of this paper include:  
1. To solve the problem of weak feature expression ability of small objects, we designed a feature extraction backbone network based on HRNet. The feature maps originating from diverse scales are processed through distinct branches to retain abundant high-resolution feature information for small objects. Simultaneously, the feature maps from different scales are fused to enhance the perception ability of the network to the objects of different scales. This approach enables the preservation of fine-grained details while enhancing the overall object detection capabilities across different scales. 2. To reduce the loss of detailed information in the process of small object feature fusion, we introduced sub-pixel convolution into multi-scale feature fusion. The upsampling process is realized by reorganizing pixel values and performing padding operations, which fully preserve the feature information of small objects.