Building an adult integrated care programme in the Top End: experience from a highly successful model of care implementation.

In the field of computer vision, traditional object detection algorithms mainly include Viola Jones Detectors, HOGDetector, and Deformable Part-based Model (DPM) that use statistical learning methods to identify objects. Figure  shows the flow chart of the traditional object detection algorithm, which plays a key role in the development and progress of early object detection technology. According to the characteristics and application purposes of data in mining production scenarios, scholars have proposed various object detection methods to meet the actual needs of mining production. Zheng Junhui et al. employed the YOLOv3 framework, incorporated the Gc Net attention module, established a reverse feature fusion pathway, and developed a three - scale prediction module. These modifications were designed to identify hazards with different dimensions and configurations, thus enhancing the robustness of the method under complex circumstances. In 2013, Jiping S et al. used the Mallat algorithm based on two-dimensional wavelet transform to preprocess the face images of miners in mines. The security monitoring system for small target detection designed by Chen Yulian organically integrates early warning functions with linkage systems, achieving favorable operational outcomes and effectively preventing the occurrence of hazards during work processes. Zhikang Chi et al.. proposed a lightweight sonar image target detection and recognition method based on frequency domain design, which improves detection efficiency. Although the above methods improve the stability of detection to different degrees, the accuracy is affected to varying degrees due to the complex underground conditions and dust, light and other factors, so this paper mainly proposes an efficient detection algorithm from the aspects of improving the yolov5 attention mechanism and loss function.