# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 5 Discussion
### (RQ6) Research Opportunities  
Public tools, such as benchmark frameworks, would allow the research community to validate its work with an impartial comparison of the published AL strategies in public datasets. However, most available tools are commercial and seem to mainly focus on uncertainty-based methods, which are, in theory, less demanding in terms of implementation.  
Therefore, pursuing the goal of obtain higher performance in instance sampling is still a valid challenge for AL researching community, which keeps designing novel methods to find instances more informative or representative for OD applications, to increase the overall performance known for the currently proposed methods. However, other criteria, such as usability and interpretability, are ignored by focusing on performance.  
In the case of interpretability [81], it would be interesting to develop AL methods that consider this criterion as it would increase the trust of the end users in the results obtained by the deep learning-based system. For usability, the emergence of methodologies for multi-oracle annotation and validation to deal with the inter- and intra- observer variability should be considered. Also, it would be interesting to include voice- and image-based technologies for assessing motivation or voice control to speed-up the annotation process. Gamification would be another approach to involving annotators in a more interactive and competitive environment, that could, simultaneously, reduce the dullness of the task.  
Additionally, other research opportunities, such as investing in one-shot AL frameworks [41; 82], fusing weak supervision techniques [68; 77], and classifiers ensembles [54] remain an open exploration path. Furthermore, there are only a few studies comparing continuous AL memory-based training to training from scratch, which could be interesting for certain applications focusing on training times reduction.  
When developing and applying AL, there are also challenges to consider and address. For each iteration of this process, the workflow needs more interaction with experts and more computing resources [41]. White methods, based on multi-models (like classifier assemblies which increase the number of parameters [48]), have a high computing cost, while black methods may not need many parameters. Meanwhile, keeping track of the detection model's performance seems to still be a challenge that can be addressed with entirely novel management strategies or by producing adaptations and combinations of existing ones.  
Moreover, collecting data for annotation from various institutions remains a barrier to be tackled [83]. This review shows that Europe is one of the main regions where the researchers are working with greater intensity on this subject. However, law-related challenges still need to be worked out, to find a more suitable balance between data access, consent to use personal information, respective anonymisation strategies, etc.  
Despite the challenges in implementing AL for OD techniques, its numerous strengths make it a compelling field of research. AL has demonstrated its effectiveness for the annotation of unlabelled instances, allowing researchers to achieve comparable performance with significantly less annotated data and training time compared to using the entire dataset. By obtaining a dataset with more representative instances, AL also reduces the inclusion of redundant samples already present in the training dataset.