Real-time Small Object Detection Model in the Bird-view UAV Imagery

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

“…It is possible to create a first classification in which we can distinguish, on the one hand a number of general-scope works [48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66] and on the other hand a larger group of publications that present domain-specific research . The first group includes studies that, far from focusing on a particular application area, propose more open research involved in the search for efficient and lightweight detection solutions, oriented to AmI scenarios.…”

“…It is used instead for practical purposes, just as a use case for quantitatively analyzing the performance of the algorithms and methods devised, as well as for assessing the feasibility of their deployment given the constraints of the hardware platforms used; merely having implications on the datasets (on the classes or object types considered) exploited for training, fine-tuning, and testing the CNNs designed. There exists a prevalence in that regard, of widespread and cross-domain AmI applications on mobile or embedded devices, such as face detection [48,58,[60][61][62] (biometric security, surveillance), and vehicle and pedestrian detection (security, surveillance, autonomous vehicles, smart cities) both in cars [56,64,65] and unmanned aerial vehicles (UAV) [49,[52][53][54][55]57,59]. [73,74,88,97,110] and smart cities [72,100,101,108], all of them, scenarios where constant and real-time object detection is necessary for enabling context-awareness on end devices.…”

“…This transfer learning approach enables reusing already existing models [48,55,63,70,75,76,[78][79][80][81][82]87,91,100,103,107], previously trained on public standard object detection benchmarks, such as Pascal VOC [115] (used in [48]), and Microsoft COCO [126] (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 [49,50,59,68,71,95,106,109], 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.…”

“…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].…”

“…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.…”