## 3 Compact and Efficient Vision-Based Detection Frameworks
### Challenges and Explored Approaches
#### 3.2.4 Hyperparameter Tuning  
This fourth and last approach encompasses a set of methods that, although barely observed in the body of works studied, has traditionally had a significant impact on ML and DL-based solutions, pursuing the proper parametrization of certain attributes either of the underlying network architectures or the specific algorithms exploited for training in order to yield a more comprehensive performance improvement of the models produced. Particularly in AmI, it has been reported in this regard, on the one hand two different approaches that involve the configuration of the anchor boxes used for the prediction tasks performed by object detection frameworks [60; 61; 79; 95] and on the other hand a better-represented approach that explores more appropriate or representative loss functions [53; 60; 64; 92; 95; 105; 107]. Anchor boxes or \"priors\" represent a concept intrinsic to modern CNN-based object detection frameworks. Thus, for every image provided as input, detectors typically produce a broad sample of anchor boxes, adopted as candidate regions examined by the algorithm to determine whether or not they contain any of the objects of interest considered, with this decision being made according to the overlapping of the anchor boxes themselves with the ground-truth boxes (a metric known as Intersection over Union, IoU). That said, a particular case is assumed to be positive when the computed overlap value is greater than a predefined threshold, triggering the refinement of the candidate region boundaries to fit the enclosed object better and thus, provide a more accurate prediction of the output bounding box. In that sense, although each feature map in the underlying CNN is likely to have several candidate regions, potentially only a small percentage of them actually contains objects, leading to a clear imbalance between positive and negative samples and consequently, to a waste of resources. Anchor box filtering [79] addresses this problem by using an object-priority-based labeling mechanism to filter out a significant chunk of the negative samples. Hence, it reduces the object search space considerably, also mitigating the detector's computational overhead. Furthermore, anchor boxes' dimensions and their aspect ratio largely determine the size and shape of the objects a detector is able to detect, to the point that if the priors do not match the different objects in the exploited dataset (for instance, because they are tiny of irregularly shaped), the detector might omit them. Motivated by this issue, two research works exploring different anchor box scaling mechanisms were identified in the analysis of on-device detectors within AmI: [59] which proposes a multiscale-box-based strategy for predicting targets with different scales and [95] that explores the utilization of small anchors to detect small and partially occluded targets. Lastly, with regards to the loss function, which drives the learning process of deep neural networks (DNN) and so it does with CNN-based object detection frameworks, it is a well-known element that has shown a significant impact on the accuracy level of the predictions produced by such algorithmic solutions. In particular, when it comes to the object detection frameworks studied, several enhancement strategies have been observed on two different types of loss: the classification loss, necessary to train detectors on object class prediction tasks, and the localization loss, required as well, to do the same, in this case, with bounding box regression. It is precisely concerning the latter, the location loss, that we find various specific optimization techniques within the AmI space. Three of them pursue a better performance in terms of accuracy, exploring possible loss factors more representative of the particular problems addressed [92; 105; 107].