Multi-object Detection in Night Time

  • Pavan Sai Vemulapalli
  • Ajay Kumar Rachuri
  • Heena Patel
  • Kishor P. Upla


This paper discusses the work on detecting multi-objects such as person and car in thermal image captured during night time using deep learning architecture. Thermal images are superior to the visible images when it comes to the amount of useful information required to detect the objects during night time. Thermal imager uses radiation emitted by the objects to create an image and improve the visibility of objects in a dark environment. Contrast to that, visible image does not provide useful information in darkness. Hence, it is better to use thermal images to detect objects present in darkness. The state-of-the-art, Yolo-v3, deep learning convolutional neural network model is the latest version of the Yolo model in which the feature extraction layer contains a much deeper network. The results of detecting person and car in the thermal images obtained by the proposed model are compared with the results of Yolo- v3. Experimental results show that there is a significant improvement in detecting person and car in the thermal images in terms of mean average precision (mAP) using the proposed method.

Keywords: Thermal imaging; Deep Learning; object detection; convolutional neural network; Yolo; Multi class detection.


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How to Cite
Vemulapalli, P. S., Rachuri, A. K., Patel, H., & Upla, K. P. (2020). Multi-object Detection in Night Time. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 5(3), 01-07. Retrieved from