# CarFree: Hassle-Free Object Detection Dataset Generation Using Carla Autonomous Driving Simulator
## 2 Related Work  
**Object detectors.** Due to the recent progress in the deep learning-based models, most modern object detection systems are based on DNNs [15; 17; 18; 19; 20; 21]. Among them, two-stage object detectors [17; 18] are usually better in their accuracy, whereas single-stage object detectors [15; 19; 20; 21] are superior in the real-time performance. To train and deploy object detection models, there are object detection frameworks such as Darknet [16] and Detectron2 [22]. In this study, we use the Darknet framework along with the YOLO object detector for both the training and the inference. Our choice is based on the fact that they are commonly used in recent autonomous driving systems [23; 24; 25].  
**Datasets.** There are famous datasets such as ImageNet [26], PASCAL VOC [27], and MS COCO [28] for training and evaluating general-purpose object detectors. However, since they include objects that are irrelevant for understanding driving scenes [29], specialized datasets for autonomous driving such as KITTI [30], Cityscape [31], Mapillary Vistas [32], and BDD100K [33] are more often used in the research community. In recent times, datasets for more complex perception tasks with multimodal sensors, such as Argoverse [34], ApolloScape [35], NuScenes [36], Waymo Open Dataset [37], and Lyft Level 5 AV Dataset [38], are being released.  
**Synthesized Datasets.** Due to the excessive efforts and costs for gathering real-world driving images, they are usually produced by big companies and institutes. Thus, generating synthetic driving scenes from 3D game engines like Unity [39] and Unreal Engine [40] is being considered as a more cost-effective alternative. Virtual KITTI [2], SYNTHIA [1], and Playing for Data [41] are some of the first such attempts. Synthetic images are especially valuable when dangerous driving scenarios or unusual whether conditions are needed, because we have the power to freely control weather in the virtual world. Even with the synthetic images, however, human labors are still required while labeling GTs for the generated images, which takes excessive time and cost. For example, it reportedly takes 88 s on average for an experienced human worker to draw and verify a single bounding box [42], which is much too burdensome.  
**Labeling tools.** There are various tools for the manual GT generation, which assist human workers to annotate the location of objects. For that, the tools provide user interfaces for drawing bounding boxes, polygons, as well as pixel-level classifications sometimes. There are well-known tools available such as CVAT [43], VoTT [44], COCO Annotator [45], VIA [46], YOLO mark [47], and LabelMe [48]. Besides, recent studies try to automatically propose GT candidates by preprocessing the images with their pre-trained object detection models [49; 50; 51]. This semi-automation helps human workers draw bounding boxes faster than they draw out of nothing but the images. Even with the above user interfaces and semi-automation methods, they are still far from full automation and impose too much burden due to the inordinate amount of images to be processed.  
**Autonomous driving simulators.** Game engine-based autonomous driving simulators have recently gained wide acceptance in the research community, as well as in the industry. In particular, open-source simulators such as AirSim [12], LGSVL Simulator [13], and Carla [11] are widely used in the research community. Among them, our study uses the Unreal Engine [40]-based Carla autonomous driving simulator, which was chosen mainly due to its rich set of Python APIs and the well-separated client-server architecture. Note that Carla is widely used for validating decision logics [52; 53; 54] and also for training agents using reinforcement learning techniques [55; 56; 57]. In some cases, Carla is used for evaluating camera-based perception systems [58] because it can generate photorealistic driving scenes.