Mask & Helmet Detection using Yolo

• Used yolov3 for multiple object detection. The biggest advantage of using YOLO is its superb speed – it's incredibly fast.

• Classes name :- F(Face),H & M (Helmet & Mask), M (Mask) ,H (Helmet)

Requiremets

pip install -r requirements.txt

Steps for object detection

• Step 1:- Converting xml to YoloV3 format

python3 custom_data/ground_truth_data_generator.py

• Step 2:- Train and Test (80:20)

python3 custom_data/train_test.py

• Step 3:- Training using darknet

./darknet detector train custom_data/detector.data custom_data/yolov3-tiny-custom.cfg darknet53.conv.74

• Step 4:- Test Inference

python3 custom_data/test_inference.py --config yolov3-tiny-custom.cfg --weights ../backup/yolov3-tiny-custom_30000.weights --names custom.names

• Step 5:- For balancing count of images having ground-truth

python3 scripts/extra/intersect-gt-and-dr.py

• Step 6:- Calculating mAp on test data

python3 custom_data/mAp_calculator.py

• For testing on real image

python3 custom_data/detection.py

• Result Plots

custom_data/output

Metrics

mAp:-

• The mAP for object detection is the average of the AP calculated for all the classes. It helps to determine how close we are in determining object and its position. It has flexibility to set threshold according to use our use case.

0ther metrics used in Object detection:-

• Precision x Recall curve