## 3 Compact and Efficient Vision-Based Detection Frameworks
### Challenges and Explored Approaches
#### 3.2.3 Improved Ad Hoc Datasets  
Datasets represent a key factor when conceiving DL-based detection frameworks. The exploitation of datasets is an unavoidable consideration for model training and evaluation, as well as for comparing different algorithmic alternatives. Indeed, the access to massive collections of annotated images and videos through the Internet has certainly played a significant role in the development of those models. The widespread availability of the so-called big data and the ubiquitous nature of modern information access technologies have enabled the compilation of extensive datasets, such as the well-known Pascal VOC [115] or Microsoft COCO [126], representative of a fair number and diversity of objects, boosting object detection in the direction of increasingly complex problems and more sophisticated solutions. Such a scenario responds, however, only to general-purpose object detection. In domains, such as AmI, where object detection is applied to specific problems, the reality differs significantly from the one described in the previous paragraph. Overall, mainstream datasets are unable to capture the singularities of the distinct class instances considered for particular use cases (especially when they are very numerous or similar), and they are not able either to do the same with the context or environment where they can commonly be found. Particularly in AmI, as seen in Section 2, there is a fair number of paradigmatic use cases that have been extensively studied, where, consequently, well-known and widely proved public databases can be found, e.g., KITTI [127] for autonomous detection systems, VisDrone [128] for object detection on images taken from UAVs, or WIDER FACE [129] for face detection. However, they do not cover in any case the broad spectrum of existing related applications in the field, and overall, they lack proper size and quality, especially when compared to general-purpose detection benchmarks. The study revealed two different approaches devised in response to the noted data-related shortcomings: the creation of ad hoc datasets for the problem addressed [50; 64; 68; 77; 78; 80; 84; 86; 91; 95; 98; 100; 103; 109], and data augmentation [48; 50; 52; 53; 57; 60; 61; 64; 70; 71; 73; 74; 76; 81; 84; 85; 87; 97; 98; 100; 106]. As shown in Table 3, both solutions have been widely adopted by the authors, a fact quite surprising considering the substantial effort typically required to address the various tasks involved in creating new datasets (image and video acquisition and homogenization, but fundamentally, the creation of annotations). In particular, the creation of customized datasets is featured in 15 of the 62 works reviewed, proving to be highly effective in dealing with the variations among the different objects within the same class and the changing environmental conditions that might affect their visual appearance. For its part, data augmentation is reported to be used in 22 studies, supplementing datasets by directly increasing their number of samples, thus avoiding classical problems in DL, such as model overfitting and class imbalance. Newly created AmI-specific datasets reportedly aggregate contents to cover as many variations as feasible (see a detailed list of common variations in AmI domains at the end of Section 3.1). The majority of those datasets are image sets created from scratch, either by actively taking pictures in real-world scenes [50; 77; 80; 84; 86; 91; 98; 100; 103; 109] or by extracting frames from previously recorded videos [64; 95] but there are also three exceptions that reuse images already present in other existing datasets [68; 78; 79]. As far as the covered domains are concerned, intelligent transport stands out in the study as the one where the creation of ad hoc datasets has aroused the most significant interest, with datasets covering both the surroundings [64; 79] and the passenger compartment of vehicles [77; 78].