Closing the Gap in Electron Detection Capabilities between SEM and TEM

The Burden of Knowledge

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Following the development of point sensing improvised explosive device (IED) technology[1] Cascade Technologies have initial work in the development of equivalent stand-off capability. Stand-off detection of IEDs is a very important technical requirement that would enable the safe identification and quantification of hazardous materials prior to a terrorist attack. This could provide advanced warning of potential danger allowing evacuation and mitigation measures to be implemented. With support from the UK government, Cascade Technologies is currently investigating technology developments aimed at addressing the above stand-off IED detection capability gap. To demonstrate and validate the concept, a novel stand-off platform will target the detection and identification of common high vapor pressure IED precursor compounds, such as hydrogen peroxide (H2O2), emanating from a point source. By actively probing a scene with polarized light, the novel platform will offer both enhanced selectivity and sensitivity as compared to traditional hyperspectral sensors, etc. The presentation will highlight the concept of this novel detection technique as well as illustrating preliminary results.

A tactical unmanned aerial vehicle-size illumination system for enhanced mine detection capabilities has been designed, developed, integrated, and tested at the Coastal Systems Station. Airborne test flights were performed from June 12, 2001 to February 1, 2002. The Airborne Laser Diode Array Illuminator uses a single-wavelength compact laser diode array stack to provide illumination and is coupled with a pair of intensified CCD video cameras. The cameras were outfitted with various lenses and polarization filters to determine the benefits of each of the configurations. The first airborne demonstration of a laser diode illumination system is described and its effectiveness to perform nighttime mine detection operations is shown. Keywords: Laser diode array, Active illumination, Nighttime reconnaissance 1. INTRODUCTION The Airborne Littoral Reconnaissance Technologies (ALRT) project at Coastal Systems Station (CSS), under direction from the Office of Naval Research (ONR), is continuing development and investigation of the Airborne Laser Diode Array Illuminator (ALDAI) prototype system, leveraged from the Joint Mine Detection Technology project, to fill the current capability gap of unmanned aerial vehicle (UAV) compatible nighttime reconnaissance, mine, and obstacle detection. This deficiency has been identified by ONR as a critical area in need of solutions. Passive electro-optical technologies lack low-light and nighttime capabilities, tending to rely on sunlight. This severely restricts their operational window. This restriction can be addressed with the addition of active illuminators. However, many illuminators are frequently not suitable for UAV deployment. They are too large, require extensive cooling, unreachable input power, or may not be powerful enough to allow for a reasonable UAV altitude. The ALDAI presents a solution to these problems. The ALDAI testbed was designed, developed, and integrated into a UAV surrogate by the CSS ALRT project team. The ALDAI system contains a compact, efficient, laser light source that transmits a fixed diverging beam and is designed to be capable of deployment in a UAV-sized platform. The system uses two single-wavelength laser diode array stacks together with a pair of intensified CCD video cameras. The cameras can be outfitted with various polarizers and filters for polarization cuing along with spectral differentiation should other wavelength diode lasers be added. From the first flight demonstration on June 12, 2001, the testbed provided measures of operational conditions, detection performance, system functional capability, and system reliability, allowing the assessment of the ALDAI system’s ability to provide enhanced detection of mines, minefields, and other trafficability issues in an airborne scenario. All aspects of performance are subordinate to sufficient ground illumination. This is primarily a function of laser power and atmospheric conditions. A baseline comparison was made by qualifying how this system compares to one operated under passive nighttime illumination sources in the visual to near infrared, especially as it relates to the moon, natural and artificial skyglow. For given laser characteristics, the two primary factors that reduce light transmission are aerosol scattering and altitude. The amount of backscatter from aerosols was compared for different polarization images. This led to specific polarizer suggestions for maximum transmission. The upper ranges of operational altitude for the ALDAI system were investigated during these tests. They can be limited by illumination power, scattering, or camera characteristics such as sensitivity, QE, and ground resolution.