  Despite the rapid advancement of object detection algorithms, processing
high-resolution images on embedded devices remains a significant challenge.
Theoretically, the fully convolutional network architecture used in current
real-time object detectors can handle all input resolutions. However, the
substantial computational demands required to process high-resolution images
render them impractical for real-time applications. To address this issue,
real-time object detection models typically downsample the input image for
inference, leading to a loss of detail and decreased accuracy. In response, we
developed Octave-YOLO, designed to process high-resolution images in real-time
within the constraints of embedded systems. We achieved this through the
introduction of the cross frequency partial network (CFPNet), which divides the
input feature map into low-resolution, low-frequency, and high-resolution,
high-frequency sections. This configuration enables complex operations such as
convolution bottlenecks and self-attention to be conducted exclusively on
low-resolution feature maps while simultaneously preserving the details in
high-resolution maps. Notably, this approach not only dramatically reduces the
computational demands of convolution tasks but also allows for the integration
of attention modules, which are typically challenging to implement in real-time
applications, with minimal additional cost. Additionally, we have incorporated
depthwise separable convolution into the core building blocks and downsampling
layers to further decrease latency. Experimental results have shown that
Octave-YOLO matches the performance of YOLOv8 while significantly reducing
computational demands. For example, in 1080x1080 resolution, Octave-YOLO-N is
1.56 times faster than YOLOv8, achieving nearly the same accuracy on the COCO
dataset with approximately 40 percent fewer parameters and FLOPs.
