  To facilitate implementation of high-accuracy deep neural networks especially
on resource-constrained devices, maintaining low computation requirements is
crucial. Using very deep models for classification purposes not only decreases
the neural network training speed and increases the inference time, but also
need more data for higher prediction accuracy and to mitigate false positives.
  In this paper, we propose an efficient and lightweight deep classification
ensemble structure based on a combination of simple color features, which is
particularly designed for "high-accuracy" image classifications with low false
positives. We designed, implemented, and evaluated our approach for explosion
detection use-case applied to images and videos. Our evaluation results based
on a large test test show considerable improvements on the prediction accuracy
compared to the popular ResNet-50 model, while benefiting from 7.64x faster
inference and lower computation cost.
  While we applied our approach to explosion detection, our approach is general
and can be applied to other similar classification use cases as well. Given the
insight gained from our experiments, we hence propose a "think small, think
many" philosophy in classification scenarios: that transforming a single,
large, monolithic deep model into a verification-based step model ensemble of
multiple small, simple, lightweight models with narrowed-down color spaces can
possibly lead to predictions with higher accuracy.
