## 3 Compact and Efficient Vision-Based Detection Frameworks
### Challenges and Explored Approaches
#### 3.2.1. Design of Novel Lightweight CNN-Based Architectures  
The design of novel lightweight CNN architectures stands out in the review as the dominant line of work, represented by the first 20 rows out of the 29 in Table 3. As its name suggests, this approach aims primarily at creating more compact and efficient network architectures (as reflected by the number of cells resulting from the intersection of the corresponding row in Table 3 with the first two columns), but it also seeks to improve accuracy in order to achieve an appropriate trade-off between speed and accuracy. Size and latency reduction go hand in hand. The more model weights, the higher the number of operations involved and consequently, the computational complexity and inference time required. Leaving aside a few exceptions in [49; 82; 86], in the AmI research analyzed, several strategies can be observed that pursue in the first place the reduction of the number of parameters, but that also affects the computational cost of the detector. In particular, it is possible to distinguish among them techniques eminently focused on the internal configuration of the layers in the network, as well as methods that, on the contrary, address global-scope factors focused on the organization of the layers or building blocks within the architecture. More specifically, within the first group, two different types of approaches have been observed: (i) a set of practices dealing with properties at the convolution kernel level (kernel number decrease, whether this is network-wide [69; 95] or more selective [50; 89]; size reduction in the spatial dimensions [95], and channel number decrease, applied, again, both indiscriminately to all the layers in the network [53], and particularly to specific stages [86] or layers [90]) and (ii) a second subset of works exploring the use of less expensive operations [49; 60; 67; 82; 95; 101], focused primarily on finding lighter alternatives to standard convolution operations, such as point-wise convolutions [95], depth-wise separable convolutions [60; 67; 101], and grouped convolutions [49]. For its part, with regard to the group dealing with higher-level matters, we find a greater heterogeneity of approaches that includes: (i) the design and integration into an existing architecture of building blocks, such as two-way dense connection modules [86], based on residual shortcut connections, or the so-called Fire modules [95; 105], consisting of a compression layer and a expansion layer, jointly capable of reducing the number of parameters of the models produced while preserving accuracy; (ii) the replacement of heavyweight backbones, such as VGG [5] by lighter networks, such as Shufflenet [49]; (iii)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model Size**} & \multirow{2}{*}{ \begin{tabular}{c} **Computational** \\ **Burden** \\ \end{tabular}  } & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Scale Variation**} & \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Scarcity**} & \multirow{2}{*}{**Quality**} \\  & & & & & & & **Variation** \\ \hline Attention & – & – & [62; 98] & – & [98] & – & – & – \\ mechanisms & & – & [60; 79] & – & [79] & – & – & – \\ \hline \multirow{4}{*}{Various receptive & – & – & – & – & [55; 59] & – & – & – \\  & & – & & & [50; 54; 57; 90; 93] & – & – & – \\ \cline{1-1} \cline{3-5} \cline{5  
the replacement of costly layers, such as fully connected layers, in which each neuron of a given layer is connected to each of the neurons in its preceding, by alternatives with fewer connections and weights, such as convolution layers [95]; (iv) shallow layer subsampling (reduction of spatial dimensions) [60], by using convolution layers with a stride value greater than 1 instead of utilizing pooling layers which result in a higher loss of detailed information. Finally, it also identified in this group more simplistic techniques, such as reducing the input dimensions of the network [50; 90] or eliminating some of its layers [76; 79; 82].