Objective

2

Home service robots have attracted widespread attention in recent years due to their close relationship with the daily lives of humans. Sweeping robots are the first home service robots that have entered the consumer market and are available extensively. At present

the intelligent sweeping robots on the market have only basic functions such as automatic path planning

automatic charging

and automatic obstacle avoidance

thereby greatly reducing the workload of housework

which is an important reason it is widely accepted by the market. Despite this situation

the current level of intelligence of sweeping robots remains low

and the main shortcomings are reflected in two aspects:First

high-level perception and discriminating ability toward the environment are lacking. For example

the behavior pattern adopted in the cleaning process is usually a random walk mode

and some higher intelligence sweeping robots may support simple path planning functions

such as taking a "Z" path. However

this function is generally classified under the "blind" mode

because the robot will perform cleaning activities whether or not garbage is present in its working path. Therefore

the work efficiency is low

and the energy consumption is greatly increased. Second

the current sweeping robots generally do not have the ability to distinguish the category of garbage. If garbage can be handled according to their correct category

then it will not only facilitate the sorting of garbage but also meet environmental protection requirements. Sweeping robots are equipped with visual sensors to achieve visual perception to improve their autonomy and intelligentization. A study of the effective classification model and algorithm of garbage detection can develop the process of location and garbage recognition

and a sweeping robot can be guided to automatically recognize and deal with different types of garbage

thereby improving the purpose and efficiency of its work. Moreover

the sweeping robot can avoid being in the blind state and reduce unnecessary energy consumption.

Method

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The proposed garbage detection and classification method based on machine vision technology aims to improve the autonomous ability of the sweeping robot. This method selects the YOLOv2 network as the main network

which has a fast detection speed in the regression method. The YOLOv2 network is combined with the dense convolutional network (DenseNet) to make full use of the high-resolution features of detection objects. The shallow and deep features of object detection can be reused and fused by embedding deep dense modules to reduce the loss of feature information. Finally

a garbage detection and classification model is built by using a multiscale strategy to train the detection network. The self-built sample dataset is used in the training and testing process. To expand the sample size

the experiment uses the image data enhancement tool ImageDataGenerator provided by Keras to perform horizontal mirror flip

random rotation

cutting

zooming

and other types of processing. The training process of the YOLOv2-dense network is as follows:First

the original image is obtained through data enhancement

and the dataset obtained by the original image and the data enhancement is manually marked. Then

YOLOv2-dense training is performed with the dataset

and the training model is obtained. To adapt large dynamic changes in the scale of the object during the movement of the mobile robot

a multiscale training strategy is adopted for network training

and the detection model is obtained. The mobile robot experiment platform uses the omnidirectional motion of the Anycbot four-wheel-drive robot chassis

the AVR motion controller

and the NVIDIA Jetson TX2 of the 256-core GPU as the visual processor.

Result

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The detection network model is trained and tested with our self-built dataset. Test results show that the improved network structure can retain more original picture information and enhance the extraction ability of target features

and this method can effectively identify the common types of garbage

including different forms of liquid and solid

and quickly and accurately mark out the location of the garbage. Real-time detection with different angles and distances presents good results. The accuracy of YOLOv2 detection is only 82.3%

and the speed is 27 frames per second. By contrast

the accuracy of the proposed improved network YOLOv2-dense reaches 84.98%

which is 2.62% higher than that of YOLOv2

and a speed of 26 frames per second can be achieved

which satisfies real-time detection.

Conclusion

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The experimental results show that the YOLOv2-dense network model built in this study has the speed of real-time detection. It shows strong adaptability and recognition performance when dealing with pictures with different backgrounds

illuminations

visual angles

and resolutions. For the same object

the detection effect is greatly changed at different angles and scales

and the detection performance of long-distance objects is poor. Moreover

for 3D objects

different viewing angles have a greater impact on detection. The result of the object detection is dynamically changed during the movement of the robot. The proposed method can ensure that the type of garbage is accurately identified most of the time or within a certain distance and angle of view. Moreover

the proposed method can guarantee the identification of garbage types with higher accuracy

and its overall performance is better than that of the original YOLOv2 model.