Failure-free operation of solar panels is of fundamental importance for modern commercial solar power plants. To achieve higher power generation efficiency and longer panel life, a simple and reliable panel evaluation method is required. By using thermal infrared imaging, anomalies can be detected without having to incorporate expensive electrical detection circuitry. In this paper, we propose a solar panel defect detection system, which automates the inspection process and mitigates the need for manual panel inspection in a large solar farm. Infrared video sequences of each array of solar panels are first collected by an infrared camera mounted to a moving cart, which is driven from array to array in a solar farm. The image processing algorithm segments the solar panels from the background in real time, with only the height of the array (specified as the number of rows of panels in the array) being given as prior information to aid in the segmentation process. In order to “count” the number the panels within any given array, frame-to frame panel association is established using optical flow. Local anomalies in a single panel such as hotspots and cracks will be immediately detected and labeled as soon as the panel is recognized in the field of view. After the data from an entire array is collected, hot panels are detected using DBSCAN clustering. On real-world test data containing over 12,000 solar panels, over 98% of all panels are recognized and correctly counted, with 92% of all types of defects being identified by the system.
KEYWORDS: Digital signal processing, Target detection, Detection and tracking algorithms, Video, Signal processing, System on a chip, Motion estimation, Cameras, Video processing, Optical flow
In this paper, we propose a real-time embedded video target tracking algorithm for use with real-world airborne video. The proposed system is designed to detect and track multiple targets from a moving camera in complicated motion scenarios such as occlusion, closely spaced targets passing in opposite directions, move-stop-move, etc. In our previous work, we developed a robust motion-based detection and tracking system, which achieved real-time performance on a desktop computer. In this paper, we extend our work to real-time implementation on a Texas Instruments OMAP 3730 ARM + DSP embedded processor by replacing the previous sequential motion estimation and tracking processes with a parallel implementation. To achieve real-time performance on the heterogeneous-core ARM + DSP OMAP platform, the C64x+ DSP core is utilized as a motion estimation preprocessing unit for target detection. Following the DSP-based motion estimation step, the descriptors of potential targets are passed to the general-purpose ARM Cortex A8 for further processing. Simultaneously, the DSP begins preprocessing the next frame. By maximizing the parallel computational capability of the DSP, and operating the DSP and ARM asynchronously, we reduce the average processing time for each video frame by up to 60% as compared to an ARM-only approach.
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