Jianguang Zhao

In complex scenarios such as low illumination and background clutter, visible-light images often suffer from missing target textures and degraded image quality. Although infrared images can complement target contour and edge information, they lack color and surface texture features. Existing visible-infrared cross-modal detection algorithms still exhibit two key shortcomings. First, the shallow features at the front end of the network lack sufficient ability to extract multi-directional and multi-scale details, resulting in weak fundamental feature representations. Second, most cross-modal fusion methods rely on feature concatenation or fixed-weight aggregation, making it difficult to effectively distinguish shared information from modality-specific complementary information and to dynamically adjust the contribution ratio of visible and infrared modalities according to scene conditions, thereby leading to significant inter-modal interference. To address these issues, this paper proposes DARF, a cross-modal detection algorithm based on YOLOv8, following the idea of front-end basic feature enhancement and back-end dual-modal adaptive fusion. Specifically, two core improvements are introduced: first, a Multi-branch Direction-Aware Feature module (MDAF) is embedded at the front end of the backbone network to enhance shallow feature representations and provide stronger support for subsequent fusion; second, a Dual-Modal Adaptive Routing fusion module (DMAR) is deployed between the backbone network and the detection neck to achieve fine-grained cross-modal adaptive feature fusion. Comparative experiments are conducted on the pixel-aligned LLVIP dataset and the KAIST dataset for cross-scene evaluation. The results show that DARF achieves higher detection accuracy than representative existing methods, thereby validating the effectiveness of the proposed modules and the overall framework.

Aiming at the phenomena of missing detection and false detection caused by multiple instances of small and medium-sized targets, occlusion between targets and rotation deviation of target positions, an improved YOLO11(You Only Look Once 11) based small target detection algorithm for UAV images is proposed. Firstly, the C3DC module is proposed by DynamicConv and C3k2 to enhance the expression ability of different features. Secondly, C2PDA is introduced into the neck by combining Dynamic Position Bias (DPB) module to reduce the sensitivity of the model to the target position deviation. Finally, SCO module is proposed to improve the feature pyramid structure to improve the detection ability of small targets. Through the experimental verification on the VisDrone2019 dataset, the experimental results show that compared with the original YOLO11 model, the mAP50 of the improved model is increased by 3.2% to 42.2%, and the MAP50:95 is increased by 2.2% to 25.6%.It achieves 33.7% mAP50 on the UAVDT dataset, which is also better than other SOTA models.Compared with the existing YOLO model and its improved version, the proposed model maintains high detection accuracy in complex scenarios. The research results show that the algorithm has significantly improved the detection ability of UAV images in complex scenes, and its comprehensive performance comprehensively exceeds that of YOLO11 model.