Abstract
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.
Highlights
Floor-cleaning robots are widely used in food courts, hospitals, industries, and homes for picking up debris and mopping floors
Customized Convolutional Neural Network (CNN) layers can heavily affect the object-detection accuracy and would not yield any better results when two object classes have very similar results. This can be overcome by combining CNN with other models such as Support Vector Machine (SVM), which can solve simple classification problems such as classification based on size
We present a debris-detection and classification task for floor-cleaning robot applications using cascaded SSD MobileNet and SVM frameworks
Summary
Floor-cleaning robots are widely used in food courts, hospitals, industries, and homes for picking up debris (dust, dirt, liquid spillage debris) and mopping floors. Machine vision-based debris recognition is an emerging technique for autonomous cleaning robots It provides an efficient solution for recognizing the debris on the floor [4,5,6]. Customized CNN layers can heavily affect the object-detection accuracy and would not yield any better results when two object classes have very similar results This can be overcome by combining CNN with other models such as SVM, which can solve simple classification problems such as classification based on size. The CNN-SVM cascaded approach takes approximately 71 milliseconds for debris detection and classification on the captured floor image It implies that the proposed technique can be implemented in real time for floor-cleaning robots to recognize debris and avoid mess which is hard to clean.
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