Extracting vehicle information is highly significant to the development of intelligent transportation systems. Vehicle logo detection (VLD) plays an important role in the extraction of vehicle information. Traditional VLD methods rely on the detection of the license plate and have difficulty in accurately locating the logo. VLD methods based on deep learning have greatly improved detection accuracy and speed. To further increase the detection accuracy and speed of VLD, a VLD method, VLDetNet, is proposed. This method first introduces a combined multibranch block for VLD (denoted as vBlock), a new network block that can be combined with other network structures to greatly improve their recognition accuracy. By combining vBlock and MobileNetV1, we then construct a new backbone network structure, vMNet, which replaces the original backbone (DarkNet53) in YOLOv3. Finally, VLDetNet extracts spatial attention information from lower-level feature maps and uses it as spatial attention weights, to mine spatial relationships of pixels and facilitate vehicle logo extraction. To further promote research on VLD, we have labeled a new vehicle logo dataset, named HFUT-VL3, which contains 6000 images and 54 types of vehicle logos obtained from an real-time highway surveillance systems in China. We conducted experiments using both HFUT-VL3 and an open VLD-45 dataset. And the experimental results demonstrate that the proposed method achieves high detection accuracy and speed and outperforms most existing methods.