Debris detection and classification is an essential function for autonomous floor-cleaning robots. It enables floor-cleaning robots to identify and avoid hard-to-clean debris, specifically large liquid spillage debris. This paper proposes a debris-detection and classification scheme for an autonomous floor-cleaning robot using a deep Convolutional Neural Network (CNN) and Support Vector Machine (SVM) cascaded technique. The SSD (Single-Shot MultiBox Detector) MobileNet CNN architecture is used for classifying the solid and liquid spill debris on the floor through the captured image. Then, the SVM model is employed for binary classification of liquid spillage regions based on size, which helps floor-cleaning devices to identify the larger liquid spillage debris regions, considered as hard-to-clean debris in this work. The experimental results prove that the proposed technique can efficiently detect and classify the debris on the floor and achieves 95.5% percent classification accuracy. The cascaded approach takes approximately 71 milliseconds for the entire process of debris detection and classification, which implies that the proposed technique is suitable for deploying in real-time selective floor-cleaning applications.
Aircraft surface inspection includes detecting surface defects caused by corrosion and cracks and stains from the oil spill, grease, dirt sediments, etc. In the conventional aircraft surface inspection process, human visual inspection is performed which is time-consuming and inefficient whereas robots with onboard vision systems can inspect the aircraft skin safely, quickly, and accurately. This work proposes an aircraft surface defect and stain detection model using a reconfigurable climbing robot and an enhanced deep learning algorithm. A reconfigurable, teleoperated robot, named as “Kiropter,” is designed to capture the aircraft surface images with an onboard RGB camera. An enhanced SSD MobileNet framework is proposed for stain and defect detection from these images. A Self-filtering-based periodic pattern detection filter has been included in the SSD MobileNet deep learning framework to achieve the enhanced detection of the stains and defects on the aircraft skin images. The model has been tested with real aircraft surface images acquired from a Boeing 737 and a compact aircraft’s surface using the teleoperated robot. The experimental results prove that the enhanced SSD MobileNet framework achieves improved detection accuracy of aircraft surface defects and stains as compared to the conventional models.
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