Smart examination classroom is an important part of smart campus, and accurately and quickly detecting students in the examination classroom is a basic task of building a smart classroom. However, due to the dense distribution and imaging difference of the examinees in an examination classroom, most of the existing object detection methods can not precisely detect all the examinees in real-time. Moreover, most of the object detection methods rely on predefined anchor boxes, which are lack of portability. Aiming at the above problems, this study proposes an efficient one-stage object detection model based on fully convolutional network, which is anchor-free, with a prediction on the input image in pixel-level. In this model, a feature enhancement module is firstly designed based on cascade attention, which can effectively enhance the discriminability of the feature map by gradually refining and modifying the features. Secondly, in order to enable the network to distinguish overlapping objects in the examination classroom, a point supervision mechanism is proposed. Finally, this study verifies the above model on the special dataset of standardized examination classroom. With the cascade attention module and point supervision mechanism, the proposed model achieves 92.9% in mAP at the speed of 22.1 f/s, and is superior to most the state-of-the-art detection models. Especially, for object detection in new classroom environments, the proposed model achieves the best results.