Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Eye movements and road hazard detection: Effects of blur and distractors

Purpose To examine the effects of optical blur, auditory distractors and age on eye movement patterns while performing a driving hazard perception test (HPT). Methods Twenty young (mean age 27.1 ± 4.6 years) and 20 older (73.3 ± 5.7 years) drivers with normal vision completed a HPT in a repeated-measures counterbalanced design while their eye movements were recorded. Testing was performed under two visual (best-corrected vision and with +2.00DS blur) and two distractor (with and without auditory distraction) conditions. Participants were required to respond to road hazards appearing in the HPT videos of real-world driving scenes and their hazard response times were recorded. Results Blur and distractors each significantly delayed hazard response time, by 0.42 and 0.76s respectively (p<0.05). A significant interaction between age and distractors indicated that older drivers were more affected by distractors than young drivers (response with distractors delayed by 0.96 and 0.60s respectively). There were no other two- or three-way interaction effect on response time. With blur, both groups fixated significantly longer on hazards before responding compared to best-corrected vision. In the presence of distractors, both groups exhibited delayed first fixation on the hazards and spent less time fixating on the hazards. There were also significant differences in eye movement characteristics between groups, where older drivers exhibited smaller saccades, delayed first fixation on hazards, and shorter fixation duration on hazards compared to the young drivers. Conclusions Collectively, the findings of delayed hazard response times and alterations in eye movement patterns with blur and distractors provide further evidence that visual impairment and distractors are independently detrimental to driving safety given that delayed hazard response times are linked to increased crash risk.