Enhancing Lightweight Neural Networks for Small Object Detection in IoT
  Applications
Liam Boyle Nicolas Baumann Seonyeong Heo Michele Magno
Advances in lightweight neural networks have revolutionized computer vision
in a broad range of IoT applications, encompassing remote monitoring and
process automation. However, the detection of small objects, which is crucial
for many of these applications, remains an underexplored area in current
computer vision research, particularly for embedded devices. To address this
gap, the paper proposes a novel adaptive tiling method that can be used on top
of any existing object detector including the popular FOMO network for object
detection on microcontrollers. Our experimental results show that the proposed
tiling method can boost the F1-score by up to 225% while reducing the average
object count error by up to 76%. Furthermore, the findings of this work suggest
that using a soft F1 loss over the popular binary cross-entropy loss can
significantly reduce the negative impact of imbalanced data. Finally, we
validate our approach by conducting experiments on the Sony Spresense
microcontroller, showcasing the proposed method's ability to strike a balance
between detection performance, low latency, and minimal memory consumption.