  To accurately and efficiently distinguish the stem end and the blossom end of
navel orange from its black spots, we propose a feature skyscraper detector
(FSD) with low computational cost, compact architecture and high detection
accuracy. The main part of the detector is inspired from small object that stem
(blossom) end is complex and black spot is densely distributed, so we design
the feature skyscraper networks (FSN) based on dense connectivity. In
particular, FSN is distinguished from regular feature pyramids, and which
provides more intensive detection of high-level features. Then we design the
backbone of the FSD based on attention mechanism and dense block for better
feature extraction to the FSN. In addition, the architecture of the detector is
also added Swish to further improve the accuracy. And we create a dataset in
Pascal VOC format annotated three types of detection targets the stem end, the
blossom end and the black spot. Experimental results on our orange data set
confirm that FSD has competitive results to the state-of-the-art one-stage
detectors like SSD, DSOD, YOLOv2, YOLOv3, RFB and FSSD, and it achieves
87.479%mAP at 131 FPS with only 5.812M parameters.
