For years, the YOLO series has been the de facto industry-level standard for efficient object
detection. The YOLO community has prospered overwhelmingly to enrich its use in a …

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

Classification and localization are two main sub-tasks in object detection. Nonetheless,
these two tasks have inconsistent preferences for feature context, ie, localization expects …

Accurate determination of the number and location of immature small yellow peaches is
crucial for bagging, thinning, and estimating yield in modern orchards. However, traditional …

Abstract You Only Look Once (YOLO) algorithms deliver state-of-the-art performance in
object detection. This research proposes a novel one-stage YOLO-based algorithm that …

Object detection is a basic vision task that accompanies people's daily lives all the time. The
development of object detection technology has experienced an evolution from traditional …

Despite much research progress in synthetic aperture radar (SAR) object detection, the
performance of SAR object detection has encountered a bottleneck limited by the imaging …

In this work, we tackle the problem of online camera-to-robot pose estimation from single-
view successive frames of an image sequence, a crucial task for robots to interact with the …

Using object detection techniques on immature fruits to find out their quantity and position is
a crucial step for intelligent orchard management. A yellow peach target detection model …

Previous object detectors make predictions based on dense grid points or numerous preset
anchors. Most of these detectors are trained with one-to-many label assignment strategies …