Improvement of small objects detection in thermal images

Abstract

Thermal images are widely used for various applications such as safety, surveillance, and Advanced Driver Assistance Systems (ADAS). However, these images typically have low contrast, blurred aspect, and low resolution, making it difficult to detect distant and small-sized objects. To address these issues, this paper explores various preprocessing algorithms to improve the performance of already trained object detection networks. Specifically, mathematical morphology is used to favor the detection of small bright objects, while deblurring and super-resolution techniques are employed to enhance the image quality. The Logarithmic Image Processing (LIP) framework is chosen to perform mathematical morphology, as it is consistent with the Human Visual System. The efficacy of the proposed algorithms is evaluated on the FLIR dataset, with a sub-base focused on images containing distant objects. The mean Average-Precision (mAP) score is computed to objectively evaluate the results, showing a significant improvement in the detection of small objects in thermal images using CNNs such as YOLOv4 and EfficientDet.