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The counting of wheat heads is labor-intensive work in agricultural production. At present, it is mainly done by humans. Manual identification and statistics are time-consuming and error-prone. With the development of machine vision-related technologies, it has become possible to complete wheat head identification and counting with the help of computer vision detection algorithms. Based on the one-stage network framework, the Wheat Detection Net (WDN) model was proposed for wheat head detection and counting. Due to the characteristics of wheat head recognition, an attention module and feature fusion module were added to the one-stage backbone network, and the formula for the loss function was optimized as well. The model was tested on a test set and compared with mainstream object detection network algorithms. The results indicate that the mAP and FPS indicators of the WDN model are better than those of other models. The mAP of WDN reached 0.903. Furthermore, an intelligent wheat head counting system was developed for iOS, which can present the number of wheat heads within a photo of a crop within 1 s.