YOLO-SDLUWD: YOLOv7-based small target detection network for infrared images in complex backgrounds

Abstract

Infrared small-target detection has important applications in many fields due to its high penetration capability and detection distance. This study introduces a detector called “YOLO-SDLUWD” which is based on the YOLOv7 network, for small target detection in complex infrared backgrounds. The “SDLUWD” refers to the combination of the spatial depth layer followed convolutional layer structure (SD-Conv) and a linear up-sampling fusion path aggregation feature pyramid network (LU-PAFPN) and a training strategy based on the normalized Gaussian Wasserstein Distance loss (WD-loss) function. “YOLO-SDLUWD” aims to solve the problem of reduced detection accuracy when the maximum pooled downsampling layer in the backbone network loses important feature information and support the interaction and fusion of high-dimensional and low-dimensional feature information and overcome the false alarm predictions caused by noise in small target images. The detector achieved a mAP@0.5 of 90.4% and mAP@0.5:0.95 of 48.5% on IRIS-AG, an increase of 9%-11% over YOLOv7-tiny, outperforming other state-of-the-art target detectors in terms of accuracy and speed.