# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 2 Contextualisation
### Active Learning in Deep Learning  
Active learning has been successful in applications involving image classification [39; 40], segmentation [41], pose estimation [7], and visual tracking [42] while its use in other tasks, such as OD, needs more attention [36]. Active learning for OD aims to decrease labelling costs by selecting the most interesting samples--depending on a certain criteria--from the unlabelled collection, aiming to improve the training effectiveness for improved detection models, allowing to reach suitable performances using a less demanding number of examples, in a particular dataset. Techniques include estimating scores either by statistical methods [36] or by using learning strategies [43]. Scores can also be computed at different levels (e.g., pixel, image [39; 44], box [45; 46], or using combinations of the former elements [47; 48]). In the methods found in literature, a wide range of sample selection criteria were explored, such as: uncertainty, diversity, density [49], rareness [15; 50], or a mixture of them to query instances according to a previously computed score.  
Active learning has been applied in various fields and for several purposes, where the main task is to detect objects. In medical applications, AL methods have used AI models to suggest which images to annotate, to reduce the workload and to optimise the available budget of specialists [41; 51]. Particularly, in digital pathology, AL can be used to annotate whole slide images (WSI) in an efficient way, since pathologists receive a reduced set of samples or patches from the set of unlabelled images. Military applications, such as aircraft detection on very high-resolution imagery, are using AL too [6], to tackle the laborious nature of the tasks involved in the annotation of WSI. In the field of autonomous driving, AL is used to annotate trajectory time-series data and discover new unknown classes [52], to locate 3D objects from 2D image detector [53], to estimate sample uncertainties [54] or diversity [16] for 2D or 3D scenarios, to perform video OD in road scenes [9], and to label portion of the dataset on a device--federated learning--avoiding the transmission of the information [34] through communication networks, among other purposes. Also, other applications benefit from AL, such as defect detection [55] and virtual reality-based pose estimation [7].  
Applying traditional AL techniques when using DL for OD poses some challenges [51]. An issue reported by authors results from the use of softmax as a score to select unlabelled instances when the current knowledge of the network can be used to select informative samples [47]. Another cause is that the models learn from many features that may not be representative, decreasing the inference capacity. Another identified cause relies in the evidence that DL algorithms perform better on batch samples than traditional techniques do on an instance-by-instance basis. Also, there are additional issues, related to the training process, to overcome [56]:  
* Dataset annotation for OD requires a bounding box and the respective category for each object in the image, making the annotation a labour-intensive task compared with classification or action recognition;
* It is challenging to identify images with real potential to improve performance;
* Finally, there are outliers and noisy instances that can affect the final performance.  
As such, there is still space for improvements in the scope of AL sampling strategies focused on OD, to more effectively reduce labelling costs while aiming at potential top-performances, which may vary in accordance with the adopted artificial network architecture, by selecting the most suitable samples from the unlabelled sets of data.  
The next chapter will address the methodology used to search, collect, and process the scientific documents considered in this review.