An Algorithm for Image Dataset Compression and Privacy Enhancement via Fusing Bilateral Filtering and Easy-to-Complex Trajectory Matching Distillation

Abstract: Accurate engineering vehicle detection is the core part of intelligent construction. Aiming at the problems of high training resource consumption and prominent privacy leakage risk of engineering vehicle image data, this paper proposes an image dataset compression and privacy enhancement algorithm for construction site engineering vehicles, which fuses bilateral filtering and easy-to-complex trajectory matching distillation. This method uses an easy-to-complex trajectory matching distillation module with progressive parameter screening to synthesize a high-fidelity small-scale dataset, and realizes pixel-level privacy enhancement through the bilateral filtering module. Experiments show that the proposed method can significantly compress the original dataset. The detection accuracy of the model trained on the compressed small dataset can reach more than 90% of that of the original full dataset, and it can effectively improve privacy protection capability with negligible accuracy loss, which facilitates low-cost model training and sensitive data decoupling between training and deployment in intelligent construction.

Qiao Zhou, Lei Zhang, Longjie Li, Tong Wang and Jun Cheng. “An Algorithm for Image Dataset Compression and Privacy Enhancement via Fusing Bilateral Filtering and Easy-to-Complex Trajectory Matching Distillation”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170409

@article{Zhou2026,
title = {An Algorithm for Image Dataset Compression and Privacy Enhancement via Fusing Bilateral Filtering and Easy-to-Complex Trajectory Matching Distillation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170409},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170409},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Qiao Zhou and Lei Zhang and Longjie Li and Tong Wang and Jun Cheng}
}