EFC-CenterNet: A Lightweight Network for Prohibited Object Detection
Cheng Han, Yinyimo Wu, Tong Jia, Dongyue Chen · 2023
Security inspection is an indispensable aspect of contemporary life and it plays a crucial role in ensuring personal safety at all times. In this regard, the accurate and prompt detection of prohibited objects is imperative. To further improve the detection accuracy of CenterNet on prohibited objects images while maintaining its high-speed detection capabilities, we make the following improvements. Firstly, to tackle the challenges related to complex and overlapping x-ray images, the Efficient Channel Attention module (ECA-Net) has been introduced to augment the feature extraction ability of the CenterNet for prohibited objects. Secondly, the Feature Pyramid Network (FPN) has been employed to amplify the feature acquisition capability of small prohibited objects. Finally, the Complete-IoU (CIoU) loss has been implemented to achieve faster convergence by minimizing the distance between the predicted bounding box and the corre-sponding ground truth while ensuring scale invariance of the two bounding boxes. The experimental results demonstrate that the EFC-CenterNet can effectively balance both accuracy and speed in real-time detection of contraband items, achieving an impressive 84.27% mAP and 52.48 FPS.