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This paper introduces a new real-time object detection approach namedYes-Net. It realizes the prediction of bounding boxes and class via singleneural network like YOLOv2 and SSD, but owns more efficient and outstandingfeatures. It combines local information with global information by adding theRNN architecture as a packed unit in CNN model to form the basic featureextractor. Independent anchor boxes coming from full-dimension k-means is alsoapplied in Yes-Net, it brings better average IOU than grid anchor box. Inaddition, instead of NMS, Yes-Net uses RNN as a filter to get the final boxes,which is more efficient. For 416 x 416 input, Yes-Net achieves 79.2% mAP onVOC2007 test at 39 FPS on an Nvidia Titan X Pascal.

Yes-Net: An effective Detector Based on Global Information