Semi-supervised Lightweight Object Detection Method Based on Improved YOLOv7-tiny

Challenges in target detection include significant variations in target size, insufficient detection accuracy on small, resource-constrained devices, and the high cost of fully annotating large datasets. To address these issues, a semi-supervised lightweight target detection method based on the improved YOLOv7-tiny algorithm is proposed. First, a new spatial pyramid pooling network, MSPP, is designed to address the problem of significant variations in target size. Then, a lightweight convolutional attention module, GC-SAM, is introduced, which generates stronger feature representations by combining channel and spatial attention mechanisms. Finally, a multi feature semi-supervised data filtering method is proposed, which enhances the model's ability to learn valuable information from unlabeled data, effectively improving detection accuracy. Experimental results show that the improved algorithm achieves a detection accuracy of 82.2% and 63.6% for mAP@50 on 30% of the KITTI and SODA1000 datasets, which is 5.4% and 2.7% higher than the original model, respectively. The model size is only 14.4 MB and the detection speed is 82.1 frames per second, confirming the effectiveness of the algorithm.