LAUSR

Active millimeter wave (AMMW) imaging technique has been widely applied in the security industries due to its under-controlled privacy concerns and no health hazards. In fact, how to automatically and precisely detect the concealed objects on the human body in the AMMW images is one key issue in industrial security scanner systems. In this paper, we first investigate the deep-learning-based object detection approaches for the AMMW images, and then, we develop a concealed object detector for the security system in the airport. For our particular application, the concealed objects include several small items such as the knife, the lighter, the phone, and so on. However, most of the current deep-learning-based methods focus mainly on detecting large objects, which occupy a large part in an image, resulting in the unsatisfactory performance on small objects. The reason lies in that the signal for the small region is rather weak before feeding into the detector. To address this issue, we first enlarge the resolution of the feature map by applying the dilated convolution. Then, we propose a context embedding object detection network in which the dilated convolutional operations with different dilations are conducted on the final feature maps. In this manner, the detector can capture both details and the context information to boost the performance of small object detection. Besides, we build a new large-scale dataset to benchmark the concealed object detection tasks in the AMMW images, which includes more than 58k AMMW images. With the proposed algorithm, we finally achieve 90.05% in detection rate and 9.73% in false alarm rate in our dataset.