A detection method based on multi-scale inverse correction enhancement and lossless downsampling is proposed to improve the detection of hidden targets in millimeter wave images with low local signal-to-noise ratio. Firstly, a multi-scale reverse correction feature enhancement module was designed, which integrates the reverse correction operation on the Res2Net multi convolution kernel. This achieves the reverse correction of convolution calculation between large receptive field regions and related small receptive field regions, enabling finer-grained features across multiple scales. Secondly, utilizing non-step convolutional layers of SPD-Conv to achieve lossless downsampling and preserve more information. Finally, the K-means++ clustering algorithm generates new anchor boxes suitable for hidden object detection tasks. The experiment selected YOLOv5s, which has moderate performance in all aspects, as the basic framework, targeting two existing millimeter wave image datasets (array image dataset and line scan image dataset) mAP@0.5 reaching 96.21% and 97.97% respectively. Compared to the original YOLOv5s and other YOLO series, the performance has significantly improved. The experimental results show that this method can effectively improve the detection performance of deep models without significantly increasing the number of parameters and inference time.