Hybrid Intersection Over Union Loss for a Robust Small Object Detection in Low-Light Conditions

In computer vision, most existing works about object detection focus on detecting objects in the good lighting conditions instead of low-light conditions. Even the few existing works that are centered on object detection in the low-light conditions, predominantly focus on the general object detection rather than the detection of small objects. The main challenges affecting small object detection accuracy in low-light conditions are occlusion caused by the low light, shadows, and darkness that adversely affect the surrounding context leading to poor object classification and the insufficient spatial information that negatively affect object localization resulting in poor small object detection. To address the challenge of poor small object detection in low-light conditions we propose the Hybrid Intersection over Union (HIoU) localization loss to enhance the detection accuracy of small objects in these conditions. This loss utilizes the top-bottom distances of the targeted and predicted bounding boxes and the manhattan distance of the boxes' centres to deal with the issue of misalignment that negatively affect the small object detection accuracy. Also, it jointly works with the classification loss to offer a joint optimization that facilitates a network to learn features that are important for both localization and classification. Experimental results show that the proposed loss enhances the detection accuracy of small objects in low-light conditions.