To accurately and efficiently distinguish the stem end and the blossom end of a 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 the stem (blossom) end is complex and the 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 dataset confirm that the 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.

为了准确而有效地将脐橙的茎端和开花端与黑点区分开，我们提出了一种功能低的摩天大楼检测器（FSD），它具有计算成本低，结构紧凑和检测精度高的特点。探测器的主要部分受小物体的启发，它的茎（开花）端很复杂，黑点密集分布，因此我们基于密集的连接性设计了摩天大楼网络（FSN）。特别是，FSN与常规特征金字塔有所区别，它可以更深入地检测高级特征。然后，基于注意力机制和密集块设计FSD的主干，以更好地提取FSN特征。此外，检测器的架构还增加了Swish以进一步提高准确性。我们创建了Pascal VOC格式的数据集，标注了三种检测目标，即茎端，开花端和黑点。我们橙色数据集上的实验结果证实，FSD相对于最先进的一级检测器（如SSD，DSOD，YOLOv2，YOLOv3，RFB和FSSD）具有竞争性结果，并且仅以131 FPS达到了87.479％的mAP 5.812M参数