## 1 Introduction  
With the continuous development of computer vision, human detection technology plays a crucial role in many practical applications. Especially in indoor environments, human detection has broad application prospects in fields such as security monitoring [1, 2], smart homes [3, 4], human-computer interaction [5], and abnormal behavior detection [6, 7], among others. However, due to the complexity of indoor scenes, factors such as changes in lighting, occlusion, background interference, and scale variations pose some challenges [8]. Although advances in deep learning technology have made some breakthroughs in this issue [9, 10], there is still a need for targeted improvements to existing technologies based on human detection in complex indoor environments. Traditional detection algorithms use manual feature detection, and the quality of manually designed features determines the accuracy of the detection algorithm [11]. The emergence of deep learning has the potential to address some of the limitations of traditional techniques [12]. Deep learning technology has become the main method of feature extraction due to its powerful learning ability and ability to express features, and it has been gradually integrated into object detection algorithms, thereby greatly improving the accuracy and efficiency of detection and enabling real-time monitoring [13, 14, 15, 16]. The ideal algorithm for human detection technology is one that achieves both high accuracy and high efficiency. The detection algorithm must be able to accurately locate and recognize the targets in every frame of an image or video while remaining unaffected by external environmental factors and inherent variability. The YOLO series has the advantages of faster detection speed and better real-time performance by transforming object detection problems into regression problems while determining the category and location of the target [13]. However, the complexity of indoor environments poses a challenge to improving the accuracy of human detection. Firstly, indoor environments typically contain various objects, furniture, and structures that may obstruct or partially hide objects of interest, resulting in obstructed and chaotic scenes. At the same time, indoor scenes also have various backgrounds, such as walls, furniture, and decorations, making the color and contour features in the image cluttered and increasing the difficulty of neural networks distinguishing effective features of human targets from the surrounding environment [8]. Secondly, the position of the camera in the indoor space may result in different perspectives and angles, affecting the appearance of the target. This can lead to significant differences and variations in the scale and size of objects in the indoor environment, from small details to large furniture, which increases the complexity of detection and recognition tasks [9; 17]. Although some progress has been made on these issues, there are still shortcomings. Thirdly, the variability of lighting in different indoor spaces may pose challenges to object detection systems, including differences in natural light, artificial lighting, shadows, and reflections [9; 18; 19]. Uneven lighting creates distinct shadows and highlights in different parts of the human body, increasing the variability of appearance. Dark lighting can blur human details, reducing the accuracy and stability of detection algorithms for human targets. Due to the combined effect of these factors, the algorithm needs to have strong light adaptability and robustness for indoor human detection tasks under complex lighting conditions. Based on the above issues, this paper proposes a novel deep learning method specifically designed for indoor human detection using the YOLOv8 architecture, called complex indoor human detection YOLO (CIHD-YOLO).