Detecting small or tiny objects is always a difficult and challenging issue in computer vision.
In this paper, we provide a latest and comprehensive survey of deep learning-based …

The data and the algorithm are critical to deep learning-based small object detectors. In this
paper, we rethink the PASCAL-VOC and MS-COCO dataset for small object detection. By …

With the continuous development of autonomous driving, real-time target detection has
become increasingly critical in autonomous driving systems. However, traditional target …

Recent CNN-based approaches have shown impressive improvements in object detection,
but detecting small objects in images is still a challenging task. Small object detection …

Average precision (AP) loss has recently shown promising performance on the dense object
detection task. However, a deep understanding of how AP loss affects the detector from a …

Conventional scene text detection approaches essentially assume that training and test data
are drawn from the same distribution and have achieved compelling results. However …

The weakly supervised object localization (WSOL) has always been a very challenging
research subject in the field of computer vision, which aims to predict the localization of …

There exists an innegligible performance gap between the binary neural networks and their
full-precision counterparts, which prevents their deployment on real-world applications …

The effectiveness of deep learning networks in detecting small objects is limited, thereby
posing challenges in addressing practical object detection tasks. In this research, we …

A shallow feature learning network (SFLN) is proposed to explore the impact of shallow
layers in object detection. In order to effectively use the shallow features and make up for the …