Light weight object detectiona lgorithm based on multiGdirectional feature pyramid

Aiming at the problems that the deep features are difficult to extract, the real-time performance cannot meet the requirements, and the bounding box positioning is not accurate enough of the real-time object detection algorithm Tiny YOLOv3, an improved lightweight detection model MTYOLO (MdFPN Tiny YOLOv3) is proposed. The model constructs multi -directional feature pyramid network (MdFPN) instead of simple concatenation to adequately complete the extraction and fusion of multi layer semantic information. The deep separable convolution is used instead of standard convolution to effectively reduce the complexity of the network and improve the real-time performance of detection. The complete IOU loss (CIOU loss) is used instead of MSE as the regression loss function, which greatly improves the regression accuracy of bounding box. The results which MTYOLO is tested on PASCAL VOC and COCO datasets show that the mAP of the improved model can reach 78.7 yo and the detection speed can reach 205 fps.