Multispectral Object Detection with Deep Learning
This work is an incremental step towards improving object detection for computer vision systems operating in challenging low-visibility environments.
This paper addresses object detection in low-visibility conditions by utilizing thermal and near-infrared (NIR) multispectral images. The authors collected their own dataset and trained a YOLO v3 network from scratch to detect objects from these multispectral images.
Object detection in natural scenes can be a challenging task. In many real-life situations, the visible spectrum is not suitable for traditional computer vision tasks. Moving outside the visible spectrum range, such as the thermal spectrum or the near-infrared (NIR) images, is much more beneficial in low visibility conditions, NIR images are very helpful for understanding the object's material quality. In this work, we have taken images with both the Thermal and NIR spectrum for the object detection task. As multi-spectral data with both Thermal and NIR is not available for the detection task, we needed to collect data ourselves. Data collection is a time-consuming process, and we faced many obstacles that we had to overcome. We train the YOLO v3 network from scratch to detect an object from multi-spectral images. Also, to avoid overfitting, we have done data augmentation and tune hyperparameters.