  Scene understanding plays an important role in several high-level computer
vision applications, such as autonomous vehicles, intelligent video
surveillance, or robotics. However, too few solutions have been proposed for
indoor/outdoor scene classification to ensure scene context adaptability for
computer vision frameworks. We propose the first Lightweight Hybrid Graph
Convolutional Neural Network (LH-GCNN)-CNN framework as an add-on to object
detection models. The proposed approach uses the output of the CNN object
detection model to predict the observed scene type by generating a coherent
GCNN representing the semantic and geometric content of the observed scene.
This new method, applied to natural scenes, achieves an efficiency of over 90\%
for scene classification in a COCO-derived dataset containing a large number of
different scenes, while requiring fewer parameters than traditional CNN
methods. For the benefit of the scientific community, we will make the source
code publicly available: https://github.com/Aymanbegh/Hybrid-GCNN-CNN.
