YOGA: Deep Object Detection in the Wild with Lightweight Feature Learning and Multiscale Attention
This work addresses the need for deployable object detection on low-end edge devices, offering a flexible and efficient solution with incremental improvements in model design.
The paper tackles the problem of efficient object detection for edge devices by introducing YOGA, a lightweight model that achieves up to a 22% increase in AP and reduces parameters and FLOPs by 23-34% compared to state-of-the-art detectors.
We introduce YOGA, a deep learning based yet lightweight object detection model that can operate on low-end edge devices while still achieving competitive accuracy. The YOGA architecture consists of a two-phase feature learning pipeline with a cheap linear transformation, which learns feature maps using only half of the convolution filters required by conventional convolutional neural networks. In addition, it performs multi-scale feature fusion in its neck using an attention mechanism instead of the naive concatenation used by conventional detectors. YOGA is a flexible model that can be easily scaled up or down by several orders of magnitude to fit a broad range of hardware constraints. We evaluate YOGA on COCO-val and COCO-testdev datasets with other over 10 state-of-the-art object detectors. The results show that YOGA strikes the best trade-off between model size and accuracy (up to 22% increase of AP and 23-34% reduction of parameters and FLOPs), making it an ideal choice for deployment in the wild on low-end edge devices. This is further affirmed by our hardware implementation and evaluation on NVIDIA Jetson Nano.