  State-of-the-art (SOTA) object detection methods have succeeded in several
applications at the price of relying on heavyweight neural networks, which
makes them inefficient and inviable for many applications with computational
resource constraints. This work presents a method to build a Convolutional
Neural Network (CNN) layer by layer for object detection from user-drawn
markers on discriminative regions of representative images. We address the
detection of Schistosomiasis mansoni eggs in microscopy images of fecal
samples, and the detection of ships in satellite images as application
examples. We could create a flyweight CNN without backpropagation from very few
input images. Our method explores a recent methodology, Feature Learning from
Image Markers (FLIM), to build convolutional feature extractors (encoders) from
marker pixels. We extend FLIM to include a single-layer adaptive decoder, whose
weights vary with the input image -- a concept never explored in CNNs. Our CNN
weighs thousands of times less than SOTA object detectors, being suitable for
CPU execution and showing superior or equivalent performance to three methods
in five measures.
