  Detecting small, densely distributed objects is a significant challenge:
small objects often contain less distinctive information compared to larger
ones, and finer-grained precision of bounding box boundaries are required. In
this paper, we propose two techniques for addressing this problem. First, we
estimate the likelihood that each pixel belongs to an object boundary rather
than predicting coordinates of bounding boxes (as YOLO, Faster-RCNN and SSD
do), by proposing a new architecture called Filter-Amplifier Networks (FANs).
Second, we introduce a technique called Loss Boosting (LB) which attempts to
soften the loss imbalance problem on each image. We test our algorithm on the
problem of detecting electrical components on a new, realistic, diverse dataset
of printed circuit boards (PCBs), as well as the problem of detecting vehicles
in the Vehicle Detection in Aerial Imagery (VEDAI) dataset.
  Experiments show that our method works significantly better than current
state-of-the-art algorithms with respect to accuracy, recall and average IoU.
