  X-ray prohibited item detection is an essential component of security check
and categories of prohibited item are continuously increasing in accordance
with the latest laws. Previous works all focus on close-set scenarios, which
can only recognize known categories used for training and often require
time-consuming as well as labor-intensive annotations when learning novel
categories, resulting in limited real-world applications. Although the success
of vision-language models (e.g. CLIP) provides a new perspectives for open-set
X-ray prohibited item detection, directly applying CLIP to X-ray domain leads
to a sharp performance drop due to domain shift between X-ray data and general
data used for pre-training CLIP. To address aforementioned challenges, in this
paper, we introduce distillation-based open-vocabulary object detection (OVOD)
task into X-ray security inspection domain by extending CLIP to learn visual
representations in our specific X-ray domain, aiming to detect novel prohibited
item categories beyond base categories on which the detector is trained.
Specifically, we propose X-ray feature adapter and apply it to CLIP within OVOD
framework to develop OVXD model. X-ray feature adapter containing three adapter
submodules of bottleneck architecture, which is simple but can efficiently
integrate new knowledge of X-ray domain with original knowledge, further bridge
domain gap and promote alignment between X-ray images and textual concepts.
Extensive experiments conducted on PIXray and PIDray datasets demonstrate that
proposed method performs favorably against other baseline OVOD methods in
detecting novel categories in X-ray scenario. It outperforms previous best
result by 15.2 AP50 and 1.5 AP50 on PIXray and PIDray with achieving 21.0 AP50
and 27.8 AP50 respectively.
