To address the phenomenon of many small and hard-to-detect objects in infrared and visible light images, we propose an object detection algorithm CDYL (Convolution to Fully Connect-ed-Deformable Convolution You only Look once) based on the CFC-DC (Convolution to Fully Connected-Deformable Convolution) module. The core operator of CDYL is CFC-DC, making our model not only have a large effective receptive field in infrared and visible light images, but also have adaptive spatial aggregation conditioned by input and task information. As a result, the CDYL reduces the strict inductive bias of traditional CNNs and has long-range dependence for large kernel convolution as well as adaptive spatial aggregation, deeply mining of edge and correlation information in images to enhance sensitivity to small objects, thereby improving performance in dense small object detection tasks. In order to improve the ability of the CFC-DC module to perceive the detailed information of the image, we use the Mish activation function, which has a wider minima which improves the generalization. The effectiveness as well as the generalization of CDYL is evaluated on an infrared image dataset and an UAV image dataset, and it is compared with other state-of-the-art object detection algorithms. Compared to the baseline network YOLOv8l, our model achieved a 3.0% improvement in mAP0.5 in infrared image detection tasks and a 1.1% improvement in mAP0.5 in visible light image detection tasks. The experimental results show that the proposed algorithm achieves superior average precision values on both infrared and visible light images, while maintaining a light weight. Code is publicly available at https://github.com/yangzhu1/CDYL.

To address the challenges of detecting a large number of objects and a high proportion of small objects in aerial drone imagery, we proposed an aerial dense small object detection algorithm called Global Normalization Attention Mechanism You Only Look Once (GNYL) based on the Global Normalization Attention Mechanism. In the backbone network of GNYL, we embedded a GNAM (Global Normalization Attention Mechanism) that explores channel attention features and spatial attention features from input features in a concatenated manner. It utilizes batch normalization’s scale factors to suppress irrelevant channels or pixels. Furthermore, the spatial attention sub-module introduces a three-dimensional arrangement with a multi-layer perceptron to reduce information loss and amplify global interaction representation. Finally, the computed attention weights are weighted to form the global normalized attention weights, which increases the utilization of effective information in input feature channels and spatial dimensions. We have optimized the backbone network, feature enhancement network, and detection heads to improve detection accuracy while ensuring a lightweight detection network. Specifically, we have added a small object detection layer to enhance the localization accuracy for the abundant small objects in aerial imagery. The algorithm’s performance was evaluated using the publicly available VisDrone2019 dataset. Compared to the baseline network YOLOv8l, GNYL achieved a 7.2% improvement in mAP_0.5 and a 5.0% improvement in mAP_0.95. Compared to CDNet, GNYL showed a 14.5% improvement in mAP_0.5 and a 9.1% improvement in mAP_0.95. These experimental results demonstrate the strong practicality of the GNYL object detection network for detecting dense small objects in the aerial imagery captured by unmanned aerial vehicles.