TrashDetector

• automatic trash separator using yolo

Development Intention

• Before explaining, this is my Capstone Design Results.
• Garbage is being dumped indiscriminately(thoughtless) on the campus.
• If it is thrown away without recycling, it will take a lot of time and manpower to classify it, so recycling is not possible now.
• So we developed the Trash Detector that encourages students to throw garbage into trash cans.

Hardware

• Raspberry 3b+
• Motor Driver - pca9685
• Servo moter - mg996r
• Unused smartphone - samsung galaxy s7

Connection Diagram

Inner Hardware Configuration

Servo moter and moter driver

Test Operation Videos

Four directions

Seperating garbage collection Without detection

Trash Detection ex

Face video & making images for train dataset

Face detection

Final result - Detecting disposable paper cup

Final result - Detecting can

The end of trash seperation

Presentation Video (korean)

Environments

• Raspbian OS
• python 3.6
• Opencv
• MTCNN

How to use

Face Detection

• First, We receive a full face video from the user, which is about 10 to 20 seconds.
• Second, in per_frame_video.py, we can split the video into a frame and save it as an image. This becomes train dataset.
• Third, in boxing.py, The mtcnn module produces an xml file whith store facial coordinates and multiple information in all images.
• The xml file is not appropriate because we are going to use yolo. Therefore, convert all xml files into txt files, i.e. yolo format. (in xml_to_yolo.py)
• And then, we already construct train dataset(face dataset), train it!

Trash Detection (NOT USAGE, WHAT I DO)

• We collected 4,000 images from the kagle, 2,000 images from the local dataset taken by ourselves, and 2,000 images from the coil dataset to form the garbage dataset.
• So, I labelled all 8,000 images myself.
• And, train it!

Files

• detection_dnn.py is detect the object and counts what kind of the trash is and returns the nearest classification(ex. plastic, paper, can.. etc).
• detection_face.py is detect the face, counts whose face it is close to, and returns the person who counts the most.
• ctr_final.py is the main file of this project. in this file, it detect the face and checks who throws it away, checks which garbage is, gives the user a reward, runs motor control, and collects it separately.

Notice

• In my repository there is no weights files, because they are heavy files. If you want our trained weights, contect me.
• Because I was in charge of object detection, I am not well aware of the applications and servers, databases, and motor control used.

Contect

• [email protected]