Aiming at the problems of low target pixels and intricate background in small target detection in infrared scenes, a target detection model based on multi-scale feature extraction with YOLOv8 was proposed. Firstly, all downsampling convolutions in the network were replaced with the Haar wavelet downsampling (HWD) module to better preserve fine-grained details in infrared imagery during downsampling. Secondly, the spatial pyramid pooling-fast (SPPF) module was improved by introducing separable convolutions, which expanded the receptive field in both horizontal and vertical directions, enabling more comprehensive spatial information capture. Furthermore, a novel C2f_CDWR module was designed using dilated convolutions with varying dilation rates to achieve adaptive feature extraction across multiple receptive fields, thus enhancing detection performance for objects of different sizes. Finally, to improve localization accuracy, the original CIoU loss in YOLOv8 was replaced with Inner-SIoU, which effectively improved bounding box regression accuracy and significantly boosted the model’s capability in detecting small infrared targets. The experimental evaluation on the HIT-UAV dataset shows that the precision of the enhanced YOLOv8 model is 90.5%, the recall rate is 75.9%, and the mean average precision is 85.7%. In terms of infrared target detection, its performance was significantly better than that of the baseline YOLOv8 model and other benchmark models.

In order to achieve more efficient detection of wearing helmets and masks in natural scenes, an improved algorithm model YOLOv5+ is proposed based on the deep learning algorithm YOLOv5. For target detection tasks, small targets are usually detected on a large feature map. Considering that most of the detected objects are small-scale targets. Therefore, when the input image size is 640×640 pixels by default, a feature map of size 160×160 pixels is added to the detection layer of the original algorithm, and complete intersection over union (CIoU) is selected as the loss function to achieve more effective detection of helmet wearing and mask wearing. The experimental results show that the mean average precision (mAP-50) of the YOLOv5+ network model reaches 93.8% and 92.3% on the helmet-wearing and mask-wearing datasets, respectively, which is both improved compared to the precision of the original algorithm. This method not only meets the speed requirement of real-time detection, but also improves the precision of detection.