YOLO-Ant: A Lightweight Detector via Depthwise Separable Convolutional
  and Large Kernel Design for Antenna Interference Source Detection
Xiaoyu Tang Xingming Chen Jintao Cheng Jin Wu Rui Fan Chengxi Zhang Zebo Zhou
In the era of 5G communication, removing interference sources that affect
communication is a resource-intensive task. The rapid development of computer
vision has enabled unmanned aerial vehicles to perform various high-altitude
detection tasks. Because the field of object detection for antenna interference
sources has not been fully explored, this industry lacks dedicated learning
samples and detection models for this specific task. In this article, an
antenna dataset is created to address important antenna interference source
detection issues and serves as the basis for subsequent research. We introduce
YOLO-Ant, a lightweight CNN and transformer hybrid detector specifically
designed for antenna interference source detection. Specifically, we initially
formulated a lightweight design for the network depth and width, ensuring that
subsequent investigations were conducted within a lightweight framework. Then,
we propose a DSLK-Block module based on depthwise separable convolution and
large convolution kernels to enhance the network's feature extraction ability,
effectively improving small object detection. To address challenges such as
complex backgrounds and large interclass differences in antenna detection, we
construct DSLKVit-Block, a powerful feature extraction module that combines
DSLK-Block and transformer structures. Considering both its lightweight design
and accuracy, our method not only achieves optimal performance on the antenna
dataset but also yields competitive results on public datasets.