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.

基于现有对象检测器，本论文提出一种新颖的自适应平铺方法，可用于微控制器上，以提高小物体的检测效果，实验证明该方法可将 F1 得分提高 225％，同时将平均物体计数误差降低至 76％，此外，结果还表明使用软 F1 损失函数可以显著减少数据不平衡带来的负面影响，并通过在索尼 Spresense 微控制器上的实验证明了该方法在检测性能、低延迟和内存消耗之间实现平衡。