Wang, Chenyang; Yan, Yan; Xue, Jing-Hao; Wang, Hanzi; (2025) I²OL-Net: Intra-Inter Objectness Learning Network for Point-Supervised X-Ray Prohibited Item Detection. IEEE Transactions on Information Forensics and Security , 20 pp. 3045-3059. 10.1109/TIFS.2025.3550052.

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Abstract

Automatic detection of prohibited items in X-ray images plays a crucial role in public security. However, existing methods rely heavily on labor-intensive box annotations. To address this, we investigate X-ray prohibited item detection under labor-efficient point supervision and develop an intra-inter objectness learning network (I2OL-Net). I2OL-Net consists of two key modules: an intra-modality objectness learning (intra-OL) module and an inter-modality objectness learning (inter-OL) module. The intra-OL module designs a local focus Gaussian masking block and a global random Gaussian masking block to collaboratively learn the objectness in X-ray images. Meanwhile, the inter-OL module introduces the wavelet decomposition-based adversarial learning block and the objectness block, effectively reducing the modality discrepancy between natural images and X-ray images and transferring the objectness knowledge learned from natural images with box annotations to X-ray images. Based on the above, I2OL-Net greatly alleviates the severe problem of part domination caused by large intra-class variations in X-ray images. Experimental results on four X-ray datasets show that I2OL-Net can achieve superior performance with a significant reduction of annotation cost, thus enhancing its accessibility and practicality. The source code is released at https://github.com/houjoeng/I2OL-Net.

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