## 3 Compact and Efficient Vision-Based Detection Frameworks
### Challenges and Explored Approaches
#### 3.2.1. Design of Novel Lightweight CNN-Based Architectures
Regarding detection accuracy, an additional fair number of approaches can be observed in the literature analyzed. They are methods and techniques that pursue better object classification performance but, eminently, go after a detection accuracy boost, especially when dealing with multiscale or tiny targets. Thus, two distinct strategies can be distinguished along those lines: broader-scoped CNN-related methods addressing classification-related concerns and object-detection-specific techniques primarily aimed at achieving higher localization accuracy. In particular, the first group encompasses solutions devised to increase the representational capacity of the networks, improving both the network's learning capacity and the accuracy yielded. Although most of the actions reported in that direction, such as increasing networks' depth [84; 96; 107] and input size [69; 102], late subsampling (i.e., reducing deep layers' size to obtain or maintain large receptive fields, thus enabling the coding of a higher volume of information [60; 79]), or increasing the number of channels in convolution kernels [102], may seem misplaced within the on-device landscape, their underlying philosophy remains entirely pertinent for the search of lightweight solutions aimed at mitigating accuracy degradation. Furthermore, those methods are presented in the AmI body of works complemented by the exploitation of more versatile techniques, such as the utilization of residual shortcut connections, a building block that has proven to be effective at enabling a better information flow throughout the network, thus mitigating the vanishing gradient problem [49; 105], and the exploitation of both convolution kernels [95] and receptive fields [55; 59] with multiple sizes, aimed explicitly at achieving higher detection accuracy when targeting multiscale objects. Target scale variation poses a challenge that commonly requires the fusion of low-level high-resolution features and high-level semantic features to obtain greater semantic richness. Data reported in this respect through Table 3, draw a scenario where the fusion of multiscale feature maps constitutes the dominant approach [53; 56; 57; 62; 64; 68; 82; 98; 102; 103; 104; 105], with this substantiated in a considerable variety of strategies: channel concatenation [53], which provides a \"trainable\" way of fusing feature maps from different stages in the network; the exploitation of the pyramidal hierarchy naturally shaped by the characteristic size-descending feature maps in CNNs [102; 103; 104]; the use of \(1\times 1\) convolution kernels right after larger convolutions [56], which is equivalent to the linear combination of the corresponding pixels along the channels in the kernel, and the exploitation of Feature Pyramid Networks (FPN) [68; 105], or any other variant, such as Deep Feature Pyramid Network (DFPN) [57], FPNLite [31], or Concatenated Feature Pyramid Network (CFPN) [82], as part of the detection framework, fusing semantic information from multiscale feature maps through an architecture comprised of an upstream and a downstream path, and the downstream path being responsible for building higher resolution feature maps using a semantically rich map as a starting point. Lastly, in addition to the several techniques just mentioned, a secondary approach has been noted in this respect, focused on investigating novel blocks or architectural elements to facilitate information transfer throughout the network underlying the detection framework. More specifically, the analyzed studies report using the Squeeze-and-Excitation blocks [62], which explicitly model channel relationships and interdependencies and include a form of self-attention on channels, and spatial attention blocks [98], which can emphasize smaller targets' features, thus capturing more important information and filtering out noise.