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.

翻译：当前最先进的目标检测方法虽在多个应用领域取得成功，但依赖重型神经网络，导致其在计算资源受限的环境中效率低下且难以部署。本文提出一种基于用户标注图像判别区域标记构建逐层卷积神经网络（CNN）的目标检测方法。以粪便样本显微镜图像中曼氏血吸虫卵检测及卫星图像中船舶检测为应用案例，我们仅需极少量输入图像即可构建无需反向传播的轻量级CNN。该方法采用最新提出的图像标记特征学习（FLIM）技术，从标记像素构建卷积特征提取器（编码器）。我们通过引入单层自适应解码器对FLIM进行扩展——该解码器权重随输入图像动态变化，这在卷积神经网络中属于全新概念。所构建的CNN参数量仅为最先进目标检测器的数千分之一，不仅适合CPU执行，且在五项评估指标中展现出优于或等效于三种对比方法的性能表现。