YOLO-GML: An object edge enhancement detection model for UAV aerial images in complex environments

by Zhihao Zheng, Jianguang Zhao, Jingjing Fan

Uav target detection is a key technology in low altitude security, disaster relief and other fields. However, in practical application scenarios, there are many complex and highly uncertain factors, such as extreme weather changes, large scale and span of the target, complex background interference, motion ambiguity, etc., which makes accurate and real-time UAV target detection still a great challenge. In order to reduce the interference of these situations in real detection scenes and improve the accuracy of UAV detection, a Global Edge Information Enhance (GEIE)module is proposed in this paper, which enables edge information to be fused into features extracted at various scales. It can improve the attention of the network to the edge information of the object. In addition, special weather conditions can greatly reduce the detection accuracy of the target, this paper proposes a Multiscale Edge Feature Enhance(MEFE) module to extract features from different scales and highlight edge information, which can improve the model’s perception of multi-scale features. Finally, we propose a Lightweight layered Shared Convolutional BN(LLSCB) Detection Head based on LSCD, so that the detection heads share the convolutional layer, and the BN is calculated independently, which improves the detection accuracy and reduces the number of parameters. A high performance YOLO detector (YOLO-GML) based on YOLO11 model is proposed. Experimental results show that Compared with YOLO11s, YOLO-GML can improve AP50 by 2.3% to 73.6% on the challenging UAV detection dataset HazyDet, achieving a better balance between accuracy and inference efficiency compared to the most advanced detection algorithms. YOLO-GML also showed good performance improvement in the SODA-A and VisDrone-2019 datasets, demonstrating the generalization of the model.

Physiological consequences of Aldolase C deficiency during lactation

by James A. Votava, Jing Fan, Brian W. Parks

The lactating mammary gland strongly induces de novo lipogenesis (DNL) to support the synthesis of fatty acids, triglycerides, and cholesterol found within milk. In monogastric species, glucose is a major substrate utilized for DNL within the lactating mammary gland and must be efficiently taken up and processed to supply cytosolic acetyl-CoA for DNL. Along with the enzymes of the DNL pathway, the glycolytic enzyme, Aldolase C (Aldoc), is transcriptionally upregulated and is highly expressed during lactation in the mammary gland, suggesting a role for Aldoc in lactation. Aldoc is also a transcriptional target of the sterol regulatory element binding proteins 1 and 2 (Srebp1 and Srebp2), which transcriptionally regulate enzymes within the DNL pathway and has recently been shown to regulate plasma cholesterol and triglycerides. Here, we investigate the role of Aldoc in lactation, by utilizing a whole-body Aldoc knockout mouse. Our results demonstrate that Aldoc has a significant impact on lactation, whereby pups nursing from Aldoc^-/- dams have reduced body weight. Biochemical analysis of milk identified that milk from Aldoc^-/- dams have significantly higher galactose, lower lactose, and cholesterol content. Mass spectrometry analysis of milk lipids from Aldoc^-/- dams revealed significantly lower quantities of medium and long chain fatty acid containing triglycerides, which has direct implications on lactation as these are the predominant triglycerides synthesized from glucose in human mammary gland. Overall, our results provide functional evidence for the contribution of Aldoc in mammary gland lactose and lipid synthesis during lactation.