Computer vision has been extensively used for livestock welfare monitoring in recent years, and data collection with a sensor or camera is the first part of the complete workflow. While current practice in computer vision-based animal welfare monitoring often analyzes data collected from a sensor or camera mounted on the roof or ceiling of a laboratory, such camera placement is not always viable in a commercial confined cattle feeding environment. This study therefore sought to determine the optimal camera placement locations in a confined steer feeding operation. Measurements of cattle pens were used to create a 3D farm model using Blender 3D computer graphic software. In the first part of this study, a method was developed to calculate the camera coverage in a 3D farm environment, and in the next stage, a genetic algorithm-based model was designed for finding optimal placements of a multi-camera and multi-pen setup. The algorithm’s objective was to maximize the multi-camera coverage while minimizing budget. Two different optimization methods involving multiple cameras and pen combinations were used. The results demonstrated the applicability of the genetic algorithm in achieving the maximum coverage and thereby enhancing the quality of the livestock visual-sensing data. The algorithm also provided the top 25 solutions for each camera and pen combination with a maximum coverage difference of less than 3.5% between them, offering numerous options for the farm manager.
Current practice for airport Pavement Management Program (PMP) inspection relies on visual surveys and manual interpretation of reports and sketches prepared by inspectors in the field to quantify pavement conditions using the Pavement Condition Index method set forth in ASTM D5340. In recent years, several attempts have been made, both by the industry and by airport operators, to use small Uncrewed (Unpersonned/Unmanned) Aircraft Systems (sUAS), or “drones,” to conduct various types of imaging and inspection of airport pavements. As part of a comprehensive study on the use of such sUAS to evaluate airfield pavement conditions, the objectives of this study were to assess the performance of various sUAS platforms and sensors in detecting and rating a subset of crack-based pavement distresses and to evaluate the use of a combination of different sUAS datasets to complement current methods used to support airport PMP. Two airports in Michigan were selected for sUAS data collection, and five sUAS platforms equipped with eight different sensors were flown at these airports at different altitudes to collect red, green, and blue (RGB) optical and thermal data at different resolutions. RGB orthophotos, digital elevation models, and thermal images were visually analyzed to study their usefulness in detecting and rating longitudinal and transverse cracks in flexible/asphalt pavements and longitudinal, transverse, and diagonal cracks, corner breaks, and durability cracks in rigid/concrete pavements. This study demonstrated the capability of using sUAS data in detecting and rating multiple crack-related distresses in both flexible and rigid airfield pavement systems.
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