# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 4 Active Learning and Object Detection: A Literature-Driven Multi-Perspective Analysis
### (RQ2) Application Domains  
In this work, the areas wherein AL techniques were applied were considered for statistical purposes ( see Table 2). In the health area, four articles articles were found [37; 57; 58; 59], making evident the lack of the use of AL to facilitate the annotation process by specialists, such as pathologists. Other applications such as document layout detection [14; 17], pose estimation [7], and surface defect detection [55] remain under-explored, as well.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **3D Context** & **2D Context** \\ \hline General—PASCAL Dataset & & [5; 18; 34; 43; 44; 45; 50; 56; 57; 60; 61; 62; 63; 64; 65;  
The implementation of AL experiments, specifically those that allow the comparison of sampling methods, does not require the intervention of the annotators. The use of public databases such as PASCAL [72], MS-COCO [73], or autonomous driving [74; 75; 76] simplifies the performance of experiments to compare sampling methods. On the other hand, in cases wherein representative datasets are required but the available data are rather insufficient; AL involving human-in-the-loop strategies to validate (accept or reject) machine-based annotation proposals stand out as a solution to progressively assemble sets of well-structured examples that sustainably support computational learning, allowing us to build increasingly effective inference models over time.  
Even though a significant part of the works found in the literature addresses OD over 2D data (see Table 2), 3D data are also focused on by other approaches (e.g., [15; 53]), in which the application of AL techniques proved to be beneficial. A method that combines 3D LiDAR and 2D data is proposed in [53], where the bounding boxes obtained by the 2D image detector are used to locate 3D objects. Afterwards, the human annotator labels only the LiDAR points with frustums. Uncertainty is estimated using three different classification predictive probabilities for comparison purposes: (i) softmax output, (ii) Monte-Carlo dropout, and (iii) deep ensembles. Later, the informativeness or Uncertainty is calculated using thShannon Entropy and Mutual Information to analyse different points of view of the epistemic uncertainty, such as predictive uncertainty and confidence in the data, respectively.  
Schmidt et al. [54] propose four ensemble-based methods to estimate sample uncertainties for 2D and 3D object detection: Consensus Score, Consensus Score Variation Ratio, Region of Interest (Rol) Matching, and Sequential RoI Matching. They propose an Ensemble-based AL method with the assumption that if Ensembles for classification are better than other techniques, the same should be observable in OD problems. Also, they consider two AL strategies for OD, namely: (i) Continuous Training (which outperforms Training from Scratch) and (ii) Active Class Weighting. This work was supported by previous works [5; 53]. Figure 6 presents the proposed setup.  
The authors of [16] proposed the use of multimodal data (GPS, IMU, and LiDAR information) in the autonomous vehicle dataset nuScenes [76] to sample instances according to pure diversity criteria. Two diversity terms, spatial- and temporal-diversity objectives,  
Figure 6: Active learning setup from [54].  
were proposed to achieve a better sampling performance when compared with the random- and entropy-based methods.