## 3 Compact and Efficient Vision-Based Detection Frameworks
### Architectures
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Detector’s Name** & \
\begin{tabular}{c} **Framework** \\ **Type** \\ \end{tabular} & **Baseline** & **Backbone** & **Pre-Training** & **Enhancement Emphasis** & **Used in** \\ \hline Tiny MetroNet & Two-stage & MetroNet & SqueezeNet & Backbone & \(\bullet\) & Lower overhead & [95] \\ – & One-stage & YOLOv4 & – & – & – & – & [94] \\ Embedded & One-stage & Tiny YOLO & – & & \(\bullet\) & Higher robustness & [93] \\ PigYOLO & One-stage & SSDLite &
\begin{tabular}{c} MobileNetV3 \\ Small \\ \end{tabular} & – & \(\bullet\) & Higher accuracy & [64] \\ Tiny Gaussian & One-stage & Tiny YOLOv3 & Darknet-19 & – & \(\bullet\) & Higher accuracy & [92] \\ YOLOv3-PDN & One-stage & YOLOv3 & Darknet-53 & – & \(\bullet\) & Lower overhead & [90] \\  & & & & & & \\  & & & & & & \\ T-TINY-YOLO & One-stage & Tiny YOLO & – & – & \(\bullet\) & Lower memory & [89] \\  & & & & & & \\  & & & & & & \\ Lira-YOLO & One-stage & YOLO & LiraNet & – & \(\bullet\) & Lower overhead & [86] \\ Fast Eye-CPU & One-stage & – & – & – & \(\bullet\) & Lower overhead & [85] \\ Multi-Task & One-stage & FaceBoxes & – & – & \(\bullet\) & Higher robustness & [62] \\ TraceBoxes & One-stage & Tiny YOLOv3 & Darknet-19 & – & \(\bullet\) & Higher robustness & [84] \\ NYOLO-tiny & One-stage & Tiny YOLOv3 & – & – & \(\bullet\) & Higher accuracy & [61] \\ Extremely Trace Detector & One-stage & – & – & – & \(\bullet\) & Higher robustness & [83] \\ DCNet & Two-stage & – & – & – & \(\bullet\) & Higher robustness & [83] \\ EagleEye & One-stage & – & VGG & – & – & [60] \\ Concatenated & Feature Pyramid & One-stage & YOLOv3 & – & Detector & \(\bullet\) & Higher robustness & [82] \\ Network & & & & & & \\ – & One-stage & YOLOv2 & Darknet-19 & Detector & \(\bullet\) & Higher robustness & [81] \\  & & & & & & \\  & & & & & & \\ – & One-stage & SSD & – & Detector & \(\bullet\) & Lower memory & [79] \\  & & & & & & \\ M3 & One-stage & YOLO9000 & – & Detector & \(\bullet\) & Higher accuracy & [76] \\ Modular Feature & One-stage & – & – & – & – & [56] \\ Fusion Detector & One-stage & – & – & – & \(\bullet\) & Lower overhead \\ – & One-stage & SSDLite & MobileNetV2 & – & \(\bullet\) & Lower memory & [74] \\  & & & & & & \\ Kankanet & One-stage & SSD & MobileNetV1 & – & – & [73] \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 2: _Cont._The unified approach, crystallized in compact architectures considered a standard nowadays, such as SSD [118] and YOLO in its different versions [119, 120, 121, 122], constitute the foundation on which modern lightweight object detection architectures have been built in AmI [48, 49, 52, 54, 55, 63, 65, 66, 68, 70, 72, 73, 75, 77, 78, 86, 88, 90, 91, 94, 100, 105, 106, 107]. Such compact architectures, however, resulted from an eminently moderate structural optimization in a first attempt to make CNNs more manageable, prioritizing accuracy preservation over structural reduction and inference speed increase. Thus, although it is possible to state that those detectors represented an effective evolution towards more efficient techniques, especially if we compare them with two-stage frameworks, they were still insufficient for running such a type of detection system on limited-resource devices mainly due to their considerable computational complexity and size. It has not been until recently when works within the on-device paradigm have actually delved into the compression and simplification of detection models and their underlying architectures, achieving a more pronounced decrease of both the number of parameters (and accordingly, the storage required) and the inference time of the detectors produced. Such research has resulted in significantly more efficient detection frameworks, making them directly deployable on low-power and low-memory hardware platforms but, on the flip side, causing a dramatic accuracy degradation.