# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
## 1 Introduction  
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. The critical challenge of floor-cleaning robots is to determine the type of debris and recognize easy-to-clean and hard-to-clean debris, specifically large liquid spillage debris. Larger debris is hard to clean for most of the floor-cleaning robots, thereby avoiding them is a better option rather than to spread them on the floor following a failed attempt to clean [1]. Recognizing and avoiding such larger debris is important for efficient cleaning by floor-cleaning robots. Typically, current floor-cleaning robots use piezoelectric debris sensors or optical sensors for recognizing debris on the floor. However, these sensing devices can identify debris only when it appears on the sensor and cannot be used for classifying debris types such as solid and liquid [1; 2; 3]. Hence, cleaning devices cannot avoid hard-to-clean liquid debris regions (e.g., large liquid spillage debris) and cannot predetermine the amount of cleaning effort required to achievebetter cleaning for small-sized liquid spillage debris [1]. 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]. [PERSON] et al. proposed a computer vision-based dusty area detection. The authors use a multivariable statistical method for recognizing the dirty areas and generate a cleanness map for the floor-cleaning robot [4]. [PERSON] et al. proposed a debris-recognition technique for floor-cleaning robots where spectral residual image filter is used for detecting the dust and dirt debris on the floor [5]. Canny edge detection and Maximally Stable Extremal Regions (MSER)-based machine vision is proposed for floor-cleaning robots to recognize and classify the dust and mud detection on the floor [7]. [PERSON] et al. suggested an unsupervised learning-based dust and dirt detection algorithm. The authors use a Gaussian Mixture Model (GMM) to recognize the dust spots on the floor [8]. [PERSON] et al. [9] developed a computer vision-based dust detection algorithm using median filter and thresholding technique where the median filter is used to remove the background of the surface and thresholding technique is used to recognize the debris on the floor. However, these techniques have shortcomings. The schemes can identify debris on the floor but cannot classify the debris types. Deep-learning-based computer vision is an emerging technique for automatic recognition and classification. Deep-learning architectures have many hidden layers of neurons and the neural network architecture can be modified and optimized to solve different complex tasks in computer vision-based applications [10; 11; 12; 13; 14; 15; 16; 17]. [PERSON] et al. [10] proposed a computer vision-based robot grasping system for automatically sorting garbage where a Fast RCNN is employed for detecting different objects in the scene. Deep-learning-based head-pose detection is proposed by [PERSON] et al. for wearable pet robots [11]. The authors use AlexNet CNN architecture for implementing the head-pose detection system. [PERSON] et al. [12] proposed a CNN-based fruit-detection system for an autonomous agricultural robotic platform. Here, the Faster RCNN framework is used for detecting the various kinds of fruit in the field. [PERSON] and [PERSON] developed a vision system for autonomous sewer robots using five-layer CNN architecture. The developed CNN architecture is used to detect and characterize cracks in a pipeline [13]. [PERSON] et al. proposed a surface defect detection algorithm. Here, Single-Shot MultiBox Detector (SSD) MobileNet network is employed to detect the surface defect [14]. Customized CNN architecture for debris classification and specific object-detection applications has been reported in [18; 19].