  Deep learning-based object detection has demonstrated a significant presence
in the practical applications of artificial intelligence. However, objects such
as fire and smoke, pose challenges to object detection because of their
non-solid and various shapes, and consequently difficult to truly meet
requirements in practical fire prevention and control. In this paper, we
propose that the distinctive fractal feature of self-similar in fire and smoke
can relieve us from struggling with their various shapes. To our best
knowledge, we are the first to discuss this problem. In order to evaluate the
self-similarity of the fire and smoke and improve the precision of object
detection, we design a semi-supervised method that use Hausdorff distance to
describe the resemblance between instances. Besides, based on the concept of
self-similar, we have devised a novel methodology for evaluating this
particular task in a more equitable manner. We have meticulously designed our
network architecture based on well-established and representative baseline
networks such as YOLO and Faster R-CNN. Our experiments have been conducted on
publicly available fire and smoke detection datasets, which we have thoroughly
verified to ensure the validity of our approach. As a result, we have observed
significant improvements in the detection accuracy.
