DLP-YOLOv9: Model with Fewer Parameters and Higher Precision Based on Improved YOLOv9 in Drone-Captured-Scenarios

With the rapid development of unmanned aerial vehicles (UAVs) technology and the increasingly extensive application fields, the accurate detection function of the objects in UAV shooting has gradually become the core issue of the industry’s frontier exploration and public attention. This paper introduces a novel object detection algorithm, designated as DLP-YOLOv9, which not only significantly improves the detection accuracy in Drone-Captured-Scenarios, but also greatly reduces the number of parameters, and realizes the dual optimization of performance and efficiency. Based on YOLOv9, we flexibly adjust the sampling position of the irregular convolution kernel to improve the accuracy of feature extraction. Meanwhile, we cleverly apply overlapping dilated convolution to propose a detection head structure DRRN, which effectively reduces parameters in YOLOv9. Finally, considering the ability to focus and process regression samples of different difficulty levels, we introduce the Focaler-CIoU method, which further improves the accuracy and efficiency of object detection. Through extensive experiments, our proposed DLP-YOLOv9 model shows excellent performance on the VisDrone2019 dataset.