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.

翻译：轻量级神经网络的进步已在广泛的物联网应用中革新了计算机视觉，涵盖远程监控和流程自动化。然而，对于许多此类应用至关重要的小目标检测，在当前计算机视觉研究中仍是一个尚未充分探索的领域，尤其是在嵌入式设备上。为解决这一空白，本文提出了一种新颖的自适应分块方法，该方法可应用于任何现有目标检测器之上，包括用于微控制器上目标检测的流行FOMO网络。我们的实验结果表明，所提出的分块方法可将F1分数提升高达225%，同时将平均目标计数误差降低高达76%。此外，本工作的发现表明，使用软F1损失替代流行的二元交叉熵损失可显著降低数据不平衡带来的负面影响。最后，我们通过在索尼Spresense微控制器上进行的实验验证了该方法，展示了其在检测性能、低延迟和最小内存消耗之间取得平衡的能力。