Browse Topic: Fatal injuries
Rotorcraft continue to experience higher fatal accident rates compared to fixed-wing aircraft, primarily due to low altitude flight operations and reduced situational awareness in complex environments. A critical factor is the limited availability of accurate, up-to-date information on helipads and surrounding obstacles - such as trees, poles, and buildings - that pose significant risks during takeoff and landing. Existing resources, including the Federal Aviation Administration's heliport registry, are often outdated and incomplete, particularly for private or state-operated sites, and fail to report nearby obstacles. This lack of up-to-date data is largely due to privacy restrictions at certain locations and the high cost associated with comprehensive obstacle surveys. To address this challenge, we develop a deep learning (DL) framework that automatically detects helipads and nearby obstacles from high-resolution satellite imagery. Our approach combines Mask R-CNN for precise pixel-level helipad segmentation with Grounding DINO, a zero-shot vision-language model that identifies obstacles using flexible text prompts (e.g., "Pole", "Tree") without task-specific training. This text-guided, scalable detection method adapts to diverse and evolving operational settings. We validate our framework across helipads in the United States, and demonstrate strong performance in both helipad localization and obstacle detection. In addition, we build a web-based application that automates image processing, updates incorrect heliport coordinates, and provides obstacle reports. This work aims to enhance aviation safety, modernize infrastructure records, and deliver scalable tools to the aviation and machine learning communities.
Prior to 1950, use of the helicopter for evacuation was extremely limited, as military top brass often considered it a worthless contraption; thus, rescue was uncertain at best for downed pilots and wounded soldiers stranded behind enemy lines. However, this all changed in Korea, where twelve U.S. Army helicopters from three detachments, working in tandem with seven, newly created Mobile Army Surgical Hospital (MASH) units, would fundamentally change the Army's medical-evacuation doctrine forever. Using several models of the Bell H-13, the Hiller H-23, and the Sikorsky H-5 and H-19, this small band of courageous pilots pushed themselves and their aircraft to their limits, transporting 21,212 critically wounded soldiers for life-saving surgery to various MASH units, cutting the fatality rate from World War II in half. Adopting the 3rd Air Rescue Squadron's motto, "That Others May Live," these pilots and their helicopters were affectionately known to the wounded as "Angels of Mercy."
ABSTRACT
Rotorcrafts are generally subject to a higher fatal accident rate than other segments of aviation, including commercial and general aviation. The safety improvement for rotorcrafts would directly improve the efficiency of air traffic control, since rotorcrafts operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants, such as unmanned aircraft systems and urban air mobility. The recent impact of artificial intelligence and deep learning algorithms on various aspects of our lives has led to the investigation of the application of these algorithms in the aviation domain; as it may offer a prime opportunity to enhance safety within the aviation community. In this research, we explore the efficacy, reliability, and, more importantly, the explainability of modern deep learning algorithms. We use machine learning models to predict the attitude (pitch and yaw) of rotorcrafts using video data recorded with ordinary cameras. The cameras were mounted inside the helicopter cockpit and recorded outside view through windshield continually during the flight. We train four different architectures of convolutional neural networks (CNNs), i.e., VGG16, VGG19, ResNet50, and Xception. The models achieved 90%, 91%, 88%, and 88%, respectively, average attitude prediction accuracy on the test video dataset. Furthermore, we use gradient class activation maps (grad-CAM) to ascertain the features and regions of the image that influenced the model to make a specific prediction. We show that CNNs learn to focus on similar features as human operators (pilots), i.e., the natural horizon curve. Our findings demonstrate the feasibility of using deep learning models for attitude prediction from f light videos recorded using ordinary inexpensive cameras. The proposed video analytics framework provides a cost-effective means to supplement traditional Flight Data Recorders (FDR); a technology that is often beyond the financial reach of most general aviation rotorcraft operators.
As the premier agency for promoting and insuring aviation safety, the Federal Aviation Administration (FAA) continues to promote and highlight the importance of participating in aviation Flight Data Monitoring (FDM) programs to improve flight safety and operational efficiency. Indeed, recorder safety is one of the agency's top 10 most wanted list of safety improvements in 2017-2018. The FAA, National Transportation Safety Board (NTSB), and the United States Helicopter Safety Team (USHST) are strong proponents of recorder use. These organizations and other industry partners are working together to implement a helicopter safety enhancement that promotes the use of flight data recorders as a mechanism to reduce the helicopter fatal accident rate. However, despite these best efforts to reduce the fatal accident rate with this lifesaving technology, barriers to implementation exist. These include initial costs of flight data recorders which can range from 9,000 - 50,000, on average. These costs can be significant for small operators and they combine to prohibit the widespread adoption of FDM by the rotorcraft community. Thus, rotorcraft, in general, typically have a lower participation rate in FDM programs than other forms of aviation (i.e. commercial fixed-wing or part 121 airline operations). On the other hand, even small helicopter operators often have access to or the financial means to purchase one or more off-the-shelf video cameras, which can be mounted inside the cockpit. These cameras offer an alternative to traditional flight data recorders as well as a means to augment them with supplementary data not always available depending on the type of Flight Data Recorder (FDR) installed in the helicopter. On board video data offers several possibilities for improving safety including flight replay, as well as the ability to extract information from the external scene such as readings of instrument panel gauges. As part of our research approach, we analyzed video data from cameras recording the instrument panel and compared these values against ground truth data from the flight data recorder. These values formed the training dataset for our video analytic framework. To analyze this information, we first cropped the gauge of interest (i.e. airspeed indicator, tachometer, engine oil temperature/pressure) in each frame of every video. The gauge image, extracted from all videos, were subsequently fed to train a deep Convolutional Neural Network (CNN) using the FDR measurements as ground truth. We trained Resnet50 CNN models for airspeed, engine oil temperature/pressure, and tachometer gauges. These models obtained 78%, 89%, 89%, and 88% validation accuracy on airspeed, engine oil temperature/pressure, and tachometer gauges, respectively. To further demonstrate the feasibility, we used the trained models to retrieve airspeed and engine oil values from the complete flight profile. We observed that the our models predicted trajectories for gauges closely follow the actual sensory values recorded by FDR. Such solution results in an effective flight data analysis tool as well as improved safety and operational efficiency of rotorcraft. These results demonstrate the feasibility of an inexpensive cockpit camera solution that would facilitate participation in FDM programs even for legacy helicopters that may otherwise require significant installation work.
Safety features introduced in recent rotorcraft designs have not made their way into the bulk of the rotorcraft flying fleets around the world in spite many of them have been firstly introduced many years ago in newly certified platforms. The longevity of the current rotorcraft population has proved to be exceeding all the expectations and forecast that were made when these features were introduced. However the flat trend in accident rates and fatalities verified in these years especially in some sectors is urging the regulators and many other stakeholders to take action. Hence the need to define a method able to establish rational priorities to push the new safety features into the market, by using quantitative and qualitative criteria.
At 1414 hours on 11 September 1970 John W. C. "Pee Wee" Judge lost control of a Wallis WA-117 autogyro and plunged to his death in front of the viewing stand at the Society of British Aerospace Companies (SBAC) air show at Farnborough. From loss of control until the fatal impact was less than 7 seconds, and as the aircraft was the center of attention (including HRH Queen Elizabeth II), it was photographed from different angles by high quality cine film cameras which enabled extensive analysis. The official accident report would not be issued for 3 and half years, essentially confirming Wing Commander Ken Wallis' own conclusions based on a frame-by-frame viewing of the films - the end result was that Wallis, the most famous autogyro pilot and popularizer since his stellar performance with his WA-116 autogyro "Little Nellie" in the 1967 James Bond film You Only Live Twice, exited from public life and pursued “the autogyro as a working aircraft” for the next 42 years. Although he would later assume the ceremonial role as “Patron of the British Rotorcraft Society” and of The Norfolk and Suffolk Aviation Museum, he steadfastly refused to facilitate construction of his autogyros by amateur builders. (Two unsuccessful models, the Wombat and the Dingbat, would eventually be built by others, the result of what Wallis would label “eyeball engineering”). His sui generis status as a 'developer' had allowed him to develop the most advanced autogyro models (and begin dominating world records for the next three decades), but the British popular rotorcraft movement would not see any benefits, and never recover from the impact in public perception and governmental skepticism as to the safety of the small autorotational aircraft. Coupled with the fact that Igor Bensen had discovered that Campbell Aircraft, its British licensee, had been selling Bensen Gyrocopter plans with its own label (and without paying royalties) and withdrawn its franchise, the popular rotorcraft movement entered into a spiral that was accentuated by the governmental scrutiny of its safety record during the 3 and half years it took to issue the Farnborough accident report, the result of which the British CAA (Civil Aviation Authority) came to be known as the "Campaign Against Aviation", a characterization still employed almost a half-century later. The "catastrophe at Farnborough" marked the beginning of the decline of the popular rotorcraft movement in Britain and to a moribund state from which it has yet to recover.
Rotorcraft with a teetering rotor design are susceptible to a phenomenon known as "mast bumping" or “excessive flapping” which can lead to severe shaft structural damage followed by total separation of the rotor from the vehicle and a potential incursion of the rotor blade into the fuselage. Mast bumping accidents are nearly always fatal and are generally unavoidable once specific flight conditions are met. Certain teetering rotor vehicles are prohibited from specific maneuvers that may lead to mast bumping events. However, specific incidents indicate that certain causes of mast bumping may have not yet been determined, and the extreme danger of the phenomenon makes studies using flight testing impossible. This research uses the Rotorcraft Comprehensive Analysis System (RCAS) to create a physics-based, parameterized model of a nominal teetering rotor helicopter to simulate and assess the mast bumping risk of various level flight conditions and specific maneuvers. This data is used to develop a metric to quantify the mast bumping risk of any maneuver. This model is also used to study the sensitivity of a vehicles mast bumping tendency to conceptual rotor design parameters. Preliminary analyses show a relationship between mast bumping risk and high airspeed, as well as low g-force. Studies on variations in blade mass properties indicate that increasing the blade mass or placing the blade CG farther towards the tip increases mast bumping risk in low speed flight regimes.
ABSTRACT A detailed review of 14CFR-Part-27/29.952-certified rotorcraft accidents from 1996 through 2015 has been performed. 58 incidents were recorded in the NTSB accident database, 48 of which were considered crashes. 12 of the accidents were rated as severe or extreme, in which 29 out of the 31 occupants onboard the rotorcraft received fatal injuries. There were three extreme crashes that resulted in significant fuel leakage and post-crash fires. According to autopsy data and the NTSB reports, all of the fatalities aboard these three extreme crashes were a result of blunt-force trauma (not thermal caused). For all of the 45 remaining crashes, there were no recorded occupant thermal injuries. All of the results indicate that rotorcraft certified to 14CFR Part 27/29.952 are performing as intended, and that the specified design / performance levels are appropriate for civil rotorcraft. Other rotorcraft that have crash-resistant fuel systems installed in them, but which are not certified to 14CFR Part 27/29.952, were not included in the analysis.
The weight of equipment bearing on the upper torso of soldiers and aviators has increased substantially over the past several decades. This increased weight of torso-supported equipment can significantly increase the chances of acute injuries during extreme events (i.e., crashes and hard landings), as well as long-term chronic injuries from normal flight operations. In this study, a novel seat subsystem (the "ActiveSpine") was developed with the goal of off-loading a seated occupant's spine and lower back from occupant-borne equipment while maintaining full mobility. By doing so, the ActiveSpine is intended to reduce pilot fatigue and risk of chronic injury during normal flight conditions, while also significantly reducing risk of injury during a crash event. In this paper, the ActiveSpine concept and initial design is summarized, and an overview of the system's active control system is provided. Following this, results of both laboratory static off-loading evaluations and full-scale dynamic crash tests are presented. Through these tests, the ActiveSpine is shown to effectively off-load the occupant borne gear mass across a range of positions, while also reducing lumbar loads in a crash by up to 35% for a range of occupant sizes. In addition to this, measured test data showed that the ActiveSpine can significantly reduce occupant head motion in a crash, thereby significantly reducing the risk of a fatal head strike.
We identify and compare the top causes for fatal and non-fatal helicopter accidents using historical accident data. We compare the causes for fatal and non-fatal accidents and identify the causes that are most likely to lead to both fatal and non-fatal accidents, the causes that are more likely to lead to fatal accidents than non-fatal accidents, and the causes that are more likely to lead to non-fatal accidents than fatal accidents. Accidents that had serious, minor, and no injuries were grouped as non-fatal accidents. We analyzed 5051 helicopter accidents between 1982 and 2008, and found that personal use, instructional flight, and aerial application missions accounted for 50.1% of the accidents. Poor weather condition was the top cause in 17.6% of fatal personal use accidents. Poor weather accidents generally occurred while operating in fog (20.4%), low ceiling (18.5%), strong tailwinds (11.1%), or rain (9.3%). Failure to maintain physical clearance (24.4%) and collision with objects were equally likely in fatal aerial application accidents. Some of the top causes appeared across mission types. Inclement weather condition was among the top causes not just during personal use, but also for instructional flights, appearing at least once in 15.3% of fatal and 12.8% of non-fatal accidents. Collision with objects was another one of the top common causes appearing across all three mission-types. By identifying the most frequent causes across different injury levels and mission types, we can leverage accident information from various sources (e.g., detailed accident reports, flight data records) to improve rotorcraft safety.
ABSTRACT Inspired watching Glenn Curtiss landing to refuel on his historic 1910 flight from Albany to New York City, the almost 5-year old John McDonald "Johnny" Miller decided he wanted to be a pilot, a decision reinforced five years later in a chance encounter with famed aviatrix Ruth Law (3rd licensed woman pilot in America) at the Curtiss Flying school in Mineola, Long Island. Miller taught himself to fly in used WWI Jenny from a text by Captain Horatio Barber, a book Miller still had in his family home in Poughkeepsie, NY eighty years later. His career in aviation, begun in a $1,500 used WWI aircraft, would span eight decades and see him as an Eastern Airline pilot flying jets - a career captured in his email address adopted in his ninth decade from jennys2jets, but Miller was most famous for being the man who beat Amelia Earhart in the first transcontinental Autogiro flight in 1931 and the 1939-1940 experimental Autogiro Airmail Route between the 30th Street Post Office roof in Philadelphia and Camden, NJ. In between, Miller supported himself with maintenance work on bootleggers airplanes and airshow performances, one of which resulted in the death of 'Al' Wilson whose replica Curtiss biplane fatally crashed in a mock dogfight with Miller's PCA-2 Autogiro. Miller's career spanned 85 years and, at his death at 102 & 1/2, he was still a licensed and active pilot. He had thrilled thousands with his Autogiro exhibitions, and while he was not the first, his daring Autogiro 'loop-the-loop' never failed to have the crowd cheering and was captured in the 1935 film Ladies Crave Excitement. Miller was an outsider - not part of the Pitcairn business enterprise which championed Earhart and, from such a truly unique vantage point, was in a special position to observe and comment. His triumphant transcontinental flight in 1931 and the 1939-1940 Autogiro Airmail Route neatly bracket the age of the American Autogiro - John McDonald Miller was part that decade, and his frequent writings provide a unique record and attest to the fabulous life of this American original.
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