Remote sensing object detection (RSOD), one of the most fundamental and challenging
tasks in the remote sensing field, has received long-standing attention. In recent years, deep …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Detecting tiny objects is one of the main obstacles hindering the development of object
detection. The performance of generic object detectors tends to drastically deteriorate on tiny …

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors,
especially for label assignment. Despite the exploration of adaptive label assignment in …

As an important component of the detector localization branch, bounding box regression
loss plays a significant role in object detection tasks. The existing bounding box regression …

With the rapid advances in deep learning techniques, remote sensing object detection
(RSOD) has achieved remarkable achievements in recent years. However, tiny object …

Video satellites can continuously image large areas and provide dynamic, real-time
monitoring of hotspots and objects. The intelligent processing and analysis of satellite video …

Occlusion between objects is one of the overlooked challenges for object detection in UAV
images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens …

Despite notable advancements in the field of computer vision (CV), the precise detection of
tiny objects continues to pose a significant challenge, largely due to the minuscule pixel …

Detecting objects from aerial images poses significant challenges due to the following
factors: 1) Aerial images typically have very large sizes, generally with millions or even …