Small object detection based on YOLOv8 in UAV perspective

Unmanned aerial vehicle (UAV) image object detection is a challenging task, primarily due to various factors such as multi-scale objects, a high proportion of small objects, significant overlap between objects, poor image quality, and complex and dynamic scenes. To address these challenges, several improvements were made to the YOLOv8 model. Firstly, by pruning the feature mapping layers responsible for detecting large objects in the YOLOv8 model, significant reduction in computational resources was achieved, rendering the model more lightweight. Simultaneously, a detection head fused with self-attention was introduced simultaneously to enhance the detection capability for small objects. Secondly, the introduction of space depth convolution in place of the original convolutional striding and pooling operations facilitates more effective preservation of details in low-resolution images and small objects. Lastly, a multi-level feature fusion module was designed to merge feature maps from different network layers, enhancing the network's representation capability. Results on the Visdrone dataset demonstrate that the proposed model achieved a significant 4.7% improvement in mAP50 compared to YOLOv8, while reducing the parameter count to only 39% of the original model. Moreover, transfer experiments on the TT100k dataset showed a 3.2% increase in mAP50, validating the effectiveness of the improved model for small object detection tasks in UAV images. Our code is made available at https://github.com/Wtgonw/Imporved-yolov8.