A Spatial Small Object Detection Algorithm Based on the Improved YOLOv8

In spatial object detection tasks, due to the far observation distance, objects often exist in the form of point objects. Due to the extremely small size of the object, the number of detectable objects is very limited, which can easily lead to missed detection and false positives in object detection. To solve these issues, we proposes a spatial object detection algorithm based on the improved YOLOv8. This algorithm enhances the network's feature extraction ability by adding GAM Attention to the Neck part of the YOLOv8 object detection algorithm, reduces information reduction, and magnifies the global interactive representations to improve the performance of deep neural networks and reduce the interference of background noise in small object detection tasks in complex environments. It introduces 3D permutation and multilayer perceptron to achieve channel attention and a convolutional spatial attention submodule to better capture the detail information of small objects. The experimental results show that the improved YOLOv8 algorithm has improved the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{mAP_{50}}$</tex> by 1.3%, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{mAP_{50-95}}$</tex> by 1.9% on the ISS dataset. In conclusion, the improved YOLOv8 can enhance the performance of small object detection.