In real life, object detection is widely applied and plays a significant part in the field of
computer vision. However, when detecting small objects, the advanced You Only Look Once …

In this paper, an improved algorithm based on Yolo V3 is proposed, which can effectively
improve the accuracy of small target detection. First of all, the feature map acquisition …

In the research of computer vision, a very challenging problem is the detection of small
objects. The existing detection algorithms often focus on detecting full-scale objects, without …

Due to the limitations of small targets in remote sensing images, such as background noise,
poor information, and so on, the results of commonly used detection algorithms in small …

Less effective information is obtained by the object detection network, due to the small size
of the detection object in the entire image, the complex background, and the dense object in …

At present, the existing methods have many limitations in small target detection, such as low
accuracy, a high rate of false detection, and missed detection. This paper proposes the KPE …

Due to their inherent characteristics, small objects have weaker feature representation after
multiple down-sampling and are even annihilated in the background. FPN's simple feature …

Small object detection has been a challenging problem in the field of object detection. There
has been some works that proposes improvements for this task, such as adding several …

Traditional camera sensors rely on human eyes for observation. However, human eyes are
prone to fatigue when observing objects of different sizes for a long time in complex scenes …

The existing object detection algorithm based on the deep convolution neural network
needs to carry out multilevel convolution and pooling operations to the entire image in order …