Fast vehicle logo detection in complex scenes

•Fast and accurate vehicle logo detection in complex scenes.•A novel VLD-30 dataset is constructed.•A CNN model is exploited to conduct feature extraction for small objects.•A supervised pre-training method is used to improve the model’s representation ability. Vehicle logo detection has received considerable attention due to its many applications, such as brand reputation measurement and vehicle monitoring. Deep-learning algorithms, such as you only look once (YOLO) and faster-regional convolutional neural network, have obtained excellent performance for object detection. However, it is not easy to exploit these methods to detect small objects. This work presents a novel method for fast and accurate vehicle logo detection (VLD) in complex scenes. First, we modify the original YOLOv3 model for the VLD task and solve the small object detection problem by hard example training. Second, we construct a new VLD dataset known as VLD-30, which encourages us to develop a data-driven training method and improve the detection accuracy. Experimental results demonstrate that the proposed data-training method is useful and the modified YOLOv3 is effective for fast and accurate vehicle logo detection in complex scenes.