The Single Shot MultiBox Detector (SSD) is one of the fastest detection algorithms.Although it has achieved good results in detection, it also has the problem of poor detection effect for small targets and occlusion between objects. Here, the authors propose a new target detection method called single-shot target detection with multi-scale feature fusion and feature enhancement. Here, the authors introduce multi-scale feature fusion module, feature enhancement module and efficient channel attention module, and integrate them into the detection module of the original SSD target detection algorithm to improve the ability of network feature extraction. Experimental results on pascal VOC 2007 datasets show that the proposed algorithm works well when the input size is 300 × 300, the detection speed reaches 41.7 frames per second (FPS) and the detection accuracy reaches 79.6%, which is 2.4% higher than the original SSD target detection algorithm. When the input size is 512 × 512, the detection accuracy is 81.9%, and the detection speed reaches 36.5 FPS, which is 3.2% higher than the original SSD target detection algorithm. According to the experimental results, our algorithm has a better performance when there are many objects in the image and there is occlusion.

No abstract

Abstract-The training quality of postgraduate students has always been a focus in the education industry. Proper ratio of tutor and students is the precondition for it. The existing tutor mode cannot afford the enrollment expansion. Tutor team is desired, which can eliminate the conflict between enrollment expansion of postgraduates and lack of tutors. A well configured tutor team can also improve the quality of both the students and the tutors. Scientific research resources and knowledge are well shared under tutor team mode. The constructing mode is proposed, including the team composing mode, tutoring pattern, team management rules and the quality monitoring system.

No abstract

No abstract