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Monocular 3D object detection has been a challenging task in the field of autonomous driving, in response to this, numerous methods have emerged accordingly. Among them, Transformer-based methods have demonstrated superior performance, which predicts 3D attributes from a single 2D image using an end-to-end approach. Most existing Transformer-based methods leverage both visual and depth representation to achieve objects detection. In these models, the quality of the learned query points has a great impact on detection accuracy. However, the existing unsupervised attention mechanism based on Transformer generates many low-quality queries due to the inaccuracy of its receptive field. To alleviate this problem, this paper introduces a novel “Edge Module” (EM) for monocular 3D object detection. Specially, EM leverage edge information to better locate the position of the object in the image and improve the accuracy of the receptive field. Specifically, the edge module also enhances low-level features, and then interactively fuses them with high-level features after optimization. After flattening the result of interaction fusion, it interacts with learnable object queries initialized by Decoder to improve the quality of object queries. Besides, we utilize a Feature Separation Module (FSM) to separate low-level features from high-level features. Then we use the edge-guided Transformer to produce edge-aware queries, which are fed into detection heads for object detection. On KITTI benchmark with monocular images as input, our method achieves state-of-the-art performance compared to the existing approaches and requires no extra data.
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