Abstract: To address the bottlenecks caused by issues such as small scales, complex and variable backgrounds, and limited computing resources in low-altitude remote sensing image object detection, a new low-altitude remote sensing image object detection method, named SimAM_YOLOv7, is proposed, based on improved YOLOv7 network. Firstly, based on tensor train decomposition, redundant parameters are minimized. Secondly, a non-parametric attention module is introduced to enhance the network's ability to focus on targets. Then, an efficient intersection over union (EIoU) is utilized to optimize the positioning loss, reducing the positional offset between the target box and the prior box. Furthermore, the classification loss is improved based on Focal Loss to overcome the imbalance between positive and negative samples. Experiments conducted on a real-world low-altitude remote sensing dataset demonstrate that, compared to the YOLOv7 baseline, the proposed method increases mAP50 by 4.63% and increases mAP50:95 by 3.94% while the number of parameters is reduced by 3.27M, fully validating its effectiveness and superiority.

Key words: tensor decomposition, attention mechanism, loss function improvement, small object detection