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.
翻译:自然场景中的天体探测可能是一项艰巨的任务。 在许多现实生活中, 可见频谱不适合传统的计算机视觉任务。 移动到可见频谱范围之外, 如热频谱或近红外图像, 在低可见度条件下更有益, NIR 图像非常有助于了解天体的物质质量。 在这项工作中, 我们为天体探测任务摄取了带有热光和 NIR 光谱的图像。 由于热光和 NIR 的多光谱数据无法用于探测任务, 我们需要自己收集数据。 数据收集是一个耗时的过程, 我们面临许多我们不得不克服的障碍。 我们从零开始训练 YOLO v3 网络, 从多光谱图像中探测物体的质量。 另外, 为避免过度匹配, 我们做了数据扩增和调超光谱仪。