220GHz wideband 3D imaging radar for concealed object detection technology development and phenomenology studies

Part of the research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 312745.We present a 220 GHz 3D imaging ‘Pathfinder’ radar developed within the EU FP7 project CONSORTIS (Concealed Object Stand-Off Real-Time Imaging for Security) which has been built to address two objectives: (i) to de-risk the radar hardware development and (ii) to enable the collection of phenomenology data with ~1 cm3 volumetric resolution. The radar combines a DDS-based chirp generator and self-mixing multiplier technology to achieve a 30 GHz bandwidth chirp with such high linearity that the raw point response is close to ideal and only requires minor nonlinearity compensation. The single transceiver is focused with a 30 cm lens mounted on a gimbal to acquire 3D volumetric images of static test targets & materials.Publisher PD

Multiplexed readout of kinetic inductance bolometer arrays

Kinetic inductance bolometer (KIB) technology is a candidate for passive sub-millimeter wave and terahertz imaging systems. Its benefits include scalability into large 2D arrays and operation with intermediate cryogenics in the temperature range of 5 -- 10 K. We have previously demonstrated the scalability in terms of device fabrication, optics integration, and cryogenics. In this article, we address the last missing ingredient, the readout. The concept, serial addressed frequency excitation (SAFE), is an alternative to full frequency-division multiplexing at microwave frequencies conventionally used to read out kinetic inductance detectors. We introduce the concept, and analyze the criteria of the multiplexed readout avoiding the degradation of the signal-to-noise ratio in the presence of a thermal anti-alias filter inherent to thermal detectors. We present a practical scalable realization of a readout system integrated into a prototype imager with 8712 detectors. This is used for demonstrating the noise properties of the readout. Furthermore, we present practical detection experiments with a stand-off laboratory-scale imager.Comment: 7 pages, 6 figure

SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, and More

The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose \textbf{SAM-Adapter}, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. Our extensive experiments show that SAM-Adapter can significantly elevate the performance of SAM in challenging tasks and we can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection and shadow detection. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more

Learning to Detect Open Carry and Concealed Object with 77GHz Radar

Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security. In this paper, we focus on the relatively unexplored area of using low-cost 77GHz mmWave radar for the carried objects detection problem. The proposed system is capable of real-time detecting three classes of objects - laptop, phone, and knife - under open carry and concealed cases where objects are hidden with clothes or bags. This capability is achieved by the initial signal processing for localization and generating range-azimuth-elevation image cubes, followed by a deep learning-based prediction network and a multi-shot post-processing module for detecting objects. Extensive experiments for validating the system performance on detecting open carry and concealed objects have been presented with a self-built radar-camera testbed and collected dataset. Additionally, the influence of different input formats, factors, and parameters on system performance is analyzed, providing an intuitive understanding of the system. This system would be the very first baseline for other future works aiming to detect carried objects using 77GHz radar.Comment: 12 page

CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models

Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. Existing COD methods primarily employ semantic segmentation, which suffers from overconfident incorrect predictions. In this paper, we propose a new paradigm that treats COD as a conditional mask-generation task leveraging diffusion models. Our method, dubbed CamoDiffusion, employs the denoising process of diffusion models to iteratively reduce the noise of the mask. Due to the stochastic sampling process of diffusion, our model is capable of sampling multiple possible predictions from the mask distribution, avoiding the problem of overconfident point estimation. Moreover, we develop specialized learning strategies that include an innovative ensemble approach for generating robust predictions and tailored forward diffusion methods for efficient training, specifically for the COD task. Extensive experiments on three COD datasets attest the superior performance of our model compared to existing state-of-the-art methods, particularly on the most challenging COD10K dataset, where our approach achieves 0.019 in terms of MAE

Fast high-resolution terahertz radar imaging at 25 meters

We report improvements in the scanning speed and standoff range of an ultra-wide bandwidth terahertz (THz) imaging radar for person-borne concealed object detection. Fast beam scanning of the single-transceiver radar is accomplished by rapidly deflecting a flat, light-weight subreflector in a confocal Gregorian optical geometry. With RF back-end improvements also implemented, the radar imaging rate has increased by a factor of about 30 compared to that achieved previously in a 4 m standoff prototype instrument. In addition, a new 100 cm diameter ellipsoidal aluminum reflector yields beam spot diameters of approximately 1 cm over a 50×50 cm field of view at a range of 25 m, although some aberrations are observed that probably arise from misaligned optics. Through-clothes images of concealed pipes at 25 m range, acquired in 5 seconds, are presented, and the impact of reduced signal-to-noise from an even faster frame rate is analyzed. These results inform the requirements for eventually achieving sub-second or video-rate THz radar imaging