Abstract
Tiny object detection is an important and challenging object detection subfield. However, many of its numerous applications (e.g., human tracking and marine rescue) have tight detection time constraints. Namely, two-stage object detectors are too slow to fulfill the real-time detection needs, whereas one-stage object detectors have an insufficient detection accuracy. Consequently, enhancing the detection accuracy of one-stage object detectors has become an essential aspect of real-time tiny objects detection. This work presents a novel model for real-time tiny objects detection based on a one-stage object detector YOLOv5. The proposed YOLO-P4 model contains a module for detecting tiny objects and a new output prediction branch. Next, a weighted bi-directional feature pyramid network (BiFPN) is introduced in YOLO-P4, yielding an improved model named YOLO-BiP4 that enhances the YOLO-P4 feature input branches. The proposed models were tested on the Tiny-Person dataset, demonstrating that the YOLO-BiP4 model outperforms the original model in detecting tiny objects. The model satisfies the real-time detection needs while obtaining the highest accuracy compared to existing one-stage object detectors.
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Hu, Y., Dai, Y., Wang, Z. (2022). Real-time Detection of Tiny Objects Based on a Weighted Bi-directional FPN. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13141. Springer, Cham. https://doi.org/10.1007/978-3-030-98358-1_1
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