## 3 Compact and Efficient Vision-Based Detection Frameworks
### Architectures
Despite the latter, these novel models, e.g., Tiny YOLO [119, 120] and SSDLite [118], are already considered standard benchmarking options when designing new lightweight detectors in AmI [50, 58, 64, 69, 74, 84, 89, 92, 93, 96, 97, 101, 102, 103, 108, 109, 110, 101, 103, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119,  
(used in [63; 75; 78; 80; 91; 107]). Still, as shown in Table 2, it is a common practice as well to exploit not the entire detection model but merely the backbone [49; 50; 59; 68; 71; 95; 109; 106], being this a CNN embedded in the detection framework, responsible for extracting from some given input images the different feature maps subsequently exploited by the deeper layers of the detector for predicting the several classes and bounding boxes produced as output. In any case, either globally or just circumscribed to the backbone, weights are initialized with values taken directly from pre-trained models, and then, through a fine-tuning process, the detector is re-trained on an application-specific dataset in order to adjust it to the specific use case to be addressed. As reflected in the \"Baseline\" column of Table 2, leaving aside some few works [56; 61; 83; 85] that have alternatively explored the creation of new architectures from scratch, the most common approach along those lines involves, however, using an existing architecture as a baseline and then either replacing the backbone [49; 52; 54; 86] or introducing changes on the detector's architecture or the training process parameterization [55; 58; 62; 69; 84; 89; 90; 93; 96; 98; 99; 101; 104; 108; 110]. Such tweaks and their focus, as seen if the \"Baseline\" and \"Enhancement Emphasis\" columns are analyzed jointly, are closely related to the very nature of the detection framework adopted as a reference. Both the \"classical\" moderate single-stage detection frameworks and the modern on-device models constitute a valuable starting point that simplifies and speeds up the creation of the new detectors. However, overall, they do not represent proper solutions for developing real-world AmI applications in austere technological environments. In particular, the more accuracy-preserving approaches, as just pointed out, fall short when it comes to optimizing the underlying network architecture and therefore, require efforts mainly aimed at minimizing the memory and computational resources demanded [48; 55; 70; 79; 82; 85; 86; 90; 107]. For its part, lightweight detectors are the result of more aggressive compression and optimization strategies. A two-fold effort is required to move towards a better speed-accuracy trade-off, on the one hand exploiting techniques capable of mitigating the accuracy degradation caused by the use of compact architectures, and on the other hand with approaches producing more expressive networks with greater detection capacity [51; 58; 59; 62; 64; 76; 84; 92; 93; 96; 97; 98; 102; 103; 108; 109]. Furthermore, in addition to the dual path of improvement indicated, there is a third line of further refinement that has pursued the creation of more robust detectors [49; 52; 54; 58; 62; 63; 65; 72; 75; 78; 80; 81; 93; 96; 97; 98; 102; 103; 105; 108; 109] no matter what their nature is. Overall, CNNs have typically shown limitations in generalizing their predictions beyond the training domain or the distribution of the used dataset. Consequently, tasks considered reasonably straightforward for humans, such as detecting faces in the wild (with different head poses and face expressions), might be very challenging for vision-based detectors. Generalization and robustness against variations have been traditionally relevant in object detection, but they have shown to be even more sensitive for on-device detection solutions conceived for AmI.