Pedestrian detection in low-resolution thermal images

Over one million people die in car accidents worldwide each year. A solution that will be able to reduce situations in which pedestrian safety is at risk has been sought for a long time. One of the techniques for detecting pedestrians on the road is the use of artificial intelligence in connection with thermal imaging. The purpose of this work was to design a system to assist the safety of people and car intelligence with the use of automatic detection of pedestrians in low-resolution thermal image sequences. The data acquisition system was designed and used to collect thermal images for the needs of training of machine learning methods. The created new dataset consists of 9178 annotated, low-resolution images of pedestrians in different traffic conditions. Several deep, object detection models were adapted and trained using the new dataset together with public datasets. The best model turned out to be the adapted Faster R-CNN ResNet50 FPN (Faster Region-based Convolutional, Neural Networks Residual network50, Feature Pyramid Network) model with mean Average Precision (mAP) equal to 94.00%. It was also shown that the use of transfer learning based on the features learned from the RGB images results in mAP greater than 85.00% for all investigated algorithms. The designed system finds practical application in increasing road safety through the potential use of autonomous cars and city monitoring.