In the process of automated crane operations for port containers, the detection of container truck heads is an indispensable link. To solve the problem of low efficiency by manual confirmation and high costs and complex systems by the laser scanning method, this study proposes an algorithm based on video images of operation scenes and deep learning for target detection of container truck heads. Specifically, upon the construction of a sample data set of container truck heads, the DCTH-YOLOv3 detection model is used, and sample training is performed through the method of model migration learning. The DCTH-YOLOv3 model is an improved YOLOv3 model proposed in this study. The algorithm improves the FPN structure of YOLOv3 and proposes a new feature pyramid structure—AF_FPN. During the fusion of higher- and lower-order features, the AFF module with the attention mechanism is introduced to focus on effective features and suppress interference noise, which increases the accuracy of detection. In addition, the metric CIoU loss is used to replace L2 loss to provide more accurate boundary box change information and further improve the model detection accuracy. The experimental results indicate that the detection rate of DCTH-YOLOv3 can reach 46 fps on GTX1080TI, which is only 3 fps lower than that of YOLOv3. The detection accuracy can reach AP_0.5 0.9974 and AP_0.9 0.4897, in which AP_0.9 is 16.4% higher than that of YOLOv3. Compared with the YOLOv3 algorithm, the proposed algorithm has higher accuracy and can better meet the requirements of automatic operations for high accuracy and fast identification in the anti-collision detection of container trucks.