In this work, an object detection method using variable convolution-improved YOLOv8 is
proposed to solve the problem of low accuracy and low efficiency in detecting spanning and …

YOLOv3 is a state-of-the-art object detection algorithm mainly used to solve real-time tasks.
However, this design struggles with scale variation, especially for extremely small or large …

The cascaded deep-learning network of YOLOv3 emphasizes on the layer-wise feature
extraction. It neglects the sequential influence among the layers that contributes to the subtle …

The speed and precision of the target detection algorithm have received wide attention in
the application. This paper proposes an improved network framework for the yolov4 …

Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is
restricted due to the low performance of its backbone network and the under-utilization of …

YOLO series are very classic detection frameworks in the field of object detection, and they
have achieved remarkable results on general datasets. Among them, YOLOv5, as a single …

Object detection is the key task for many Unmanned Aerial Vehicles (UAVs) tasks.
Compared with images captured from ground-based angles, images captured by UAVs are …

In this paper we propose a lightweight object detector by optimizing the structure of YOLOv3.
The optimization is conducted in two aspects: simplifying the structural components by …

Detecting small objects in complex circumstances is an important topic in the research of
today's object detection [1], especially in military, which needs more reliable, stable and …

The YOLOv5 network architecture prioritizes speed and efficiency, but this may limit its ability
to capture intricate details of complex objects. To solve the problems of insufficient feature …