In this paper, aiming at the complex background and overlapping characteristics in X-ray images, we propose an unique spatial attention mechanism based on the feedback of high-level semantic feature to guide low-level semantic features, named Feedback Guidance Mechanism (FGM). In addition, in view of the high probability of miss of small prohibited items, a feature aggregation method based on the fusion of high and low-level features and dilated convolution is proposed, named Feature Aggregation Module (FAM). Then, we combine FGM and FAM into a lightweight model SSD and get a new Prohibited Items Detector (PIXDet). Our experiments indicate that PIXDet is more lightweight, but it can achieve 90.36% mAP on PIXray dataset, exceeding SSD by 1.0% mAP, outperforming some state-of-the-art methods, implying its potential applications in prohibited items detection field.
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