## 5 Object Detection Application Domains
### Intelligent Transportation Systems  
The use of intelligent transportation systems (ITS) facilitates people's lives, and they cover areas such as road sign recognition, advanced driver assistance systems, license plate recognition, vehicle detection, vehicle speed estimation, and driver awareness monitoring systems.  
Both UAV and self-driving cars require real-time accurate traffic sign recognition for the safety of both passengers and surroundings. [PERSON] et al. [200] proposed a branch output mechanism into a deep CNN to speed up traffic sign recognition. [PERSON] et al. [201] proposed a CNN with input fed from the simplified Gabor feature map to classify the traffic signs on Chinese and German databases. [PERSON] et al. [202] proposed \"improved Faster RCNN for traffic sign detection\" in real traffic situations using a \"highly possible regions proposal network (HP-RPN)\".  
[PERSON] et al. [203] proposed the classical LeNet-5 CNN model to improve traffic sign detection for intelligent vehicles. [PERSON] et al. [204] proposed an end-to-end improved YOLOv2 to achieve real-time Chinese traffic sign detection. [PERSON] et al. [205] multi-task CNN to recognize all traffic signs classes which include both symbol-based and text-based signs. [PERSON] et al. [206] proposed a Faster RCNN and the MobileNet framework that can detect all categories of traffic signs, wherein color and shape information is used to refine localization of small traffic signs.  
Recently deep learning-based methods were applied for \"automatic license plate recognition\" (ALPR), detecting driver's drowsiness on highways, traffic violations, etc. [PERSON] et al. [207] proposed license plate recognition using a sequence of deep CNNs, and the system is robust under different conditions (lighting, occlusions, variations in the pose). [PERSON] et al. [208] proposed an efficient YOLO detector for automatic License plate recognition, and it is robust under different conditions. [PERSON] [209] proposed the ALPR system via sliding window + darknet YOLO tested on the AOLP dataset. [PERSON] et al. [210] proposed a multi-channel CNN with an aggression module and SVM to achieve a high recognition rate on multi-national vehicle license plates under various illumination conditions. [PERSON] et al. [211] implemented real-time ALPR via two deep multi-task networks.  
An accurate perception of its surroundings is necessary for an autonomous vehicle (AV). To enable autonomous driving using deep learning techniques, one should convert collected data from sensors into semantic information. For the autonomous driving system, 3D object detection is preferred over 2D object detection, since the third dimension provides more detailed information on size and exact object locations.  
[PERSON] et al. [212] extended the \"3 DOP proposal generation considering class-independent proposals, then re-rank[ed] the proposals\" using both monocular images and depth maps. [PERSON] et al. [213] used \"a cylindrical projection mapping and a fully-convolutional network (FCN) to predict 3D bounding boxes around vehicles only\" [PERSON] et al. [214] proposed that, \"Frustum Point-Net generates region proposals on the image plane with monocular images and use[s] the point cloud\" to perform classification and bounding box regression.