ComNet: Combinational Neural Network for Object Detection in UAV-Borne Thermal Images

Minglei Li, Xingke Zhao, Jiasong Li, L. Nan

Research output: Contribution to journalArticleScientificpeer-review

22 Citations (Scopus)
97 Downloads (Pure)


We propose a deep learning-based method for object detection in UAV-borne thermal images that have the capability of observing scenes in both day and night. Compared with visible images, thermal images have lower requirements for illumination conditions, but they typically have blurred edges and low contrast. Using a boundary-aware salient object detection network, we extract the saliency maps of the thermal images to improve the distinguishability. Thermal images are augmented with the corresponding saliency maps through channel replacement and pixel-level weighted fusion methods. Considering the limited computing power of UAV platforms, a lightweight combinational neural network ComNet is used as the core object detection method. The YOLOv3 model trained on the original images is used as a benchmark and compared with the proposed method. In the experiments, we analyze the detection performances of the ComNet models with different image fusion schemes. The experimental results show that the average precisions (APs) for pedestrian and vehicle detection have been improved by 2%~5% compared with the benchmark without saliency map fusion and MobileNetv2. The detection speed is increased by over 50%, while the model size is reduced by 58%. The results demonstrate that the proposed method provides a compromise model, which has application potential in UAV-borne detection tasks.
Original languageEnglish
Pages (from-to)6662-6673
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number8
Publication statusPublished - 2021

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