# Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
## 5 Discussion
### (Rq2) Application Domains  
Generally, in an object detection task, multiple target objects may appear in a single image. Therefore, sampling strategies should deal with each object's information (bounding box, class, and confidence)--which contrasts with sampling strategies for classification that only need to address confidence. If an appropriate aggregation method is not considered (e.g., maximum focus on outliers, average weight of all objects equally, sum is dependent on the number of objects), the sampling may perform poorly as a relevant contributor to smoothing annotation efforts, as well as to promote the construction of increasingly robust models. Multiple alternatives include the aggregation of information from multiple sources such as: pixel-level, box-level [45] or image-level [44; 47], which have been useful for both uncertainty-based and diversity-based methods, and more specifically to guarantee the reduction of labelled instances overlapping, while optimising data contributions aiming at models with increasingly improved performance.  
AL strategies can be designed with a focus on issues that reduce the performance of object detection methods. Examples include class-imbalance, poor diversity, rareness, or outlier instances. Feature distribution-based methods compensate for the lack of exploration of uncertainty-based methods. Clustering algorithms, for example, have been widely applied to optimise criteria such as diversity [49], representativeness [67], rarity [15; 50], and density [16; 78]. However, the proposed methods do not always fully explore their potential by focusing on an exclusive criteria--among the previously referenced ones--when considering several complementary sampling strategies simultaneously is possible [36; 37; 44; 45; 57; 61; 67].  
Moreover, object detection applications are concerned with the location and classification of objects in an image. The classifiers used for uncertainty estimation ignore the location information [48]. Therefore, a good instance selection method for object detection must consider the convolutional neural network's ability to classify and locate, even in conditions wherein it is challenging to determine object-location informativeness [5]. The acquisition function should be able to measure these couple of features out of a neural network to produce reliable instance rankings. The possibility of unsuccessfully combining two or more sampling strategies should be considered a weakness. Applying one type of sampling strategy (e.g., uncertainty) tends to select similar images [6], resulting in an insufficient approach [37].  
The application of an AL strategy may be regulated for several reasons. As for any validation process involving humans, the oracle verification process must insure the inclusion of intra- and inter-group variability. Also, it is important to avoid situations wherein annotators spend or exceed the expected budget before checking the entire stream-based selective sampling scenario. With these issues in mind, AL approaches allow for not only the reduction of human effort, optimising the annotation process, but also for increasing the ability to manage the aspects regarding the variability of the verification process, as well as the monitoring of the available annotation budget along the operationalisation steps [6].  
Another conclusion drawn from this work, which can be also observed in Table 2, relates to the predominance of data dimension addressed in AL, which is mostly 2D. This observation may be corroborated by the following factor: data with other spectral and spatial dimensionalities (3D, multi/hyperspectral, etc.) are usually more demanding and expensive to acquire--this is even more aggravated if precision is required--since costly devices, preparation, and/or pre-processing steps are often involved, as well. Having said that, great and interesting opportunities to keep exploring the application of AL and OD can be glimpsed and pursued.