Channel Compression Optimization Oriented Bus Passenger Object Detection

Abstract

Bus passenger flow information can facilitate scientific dispatching plans, which is essential to decision making and operation performance evaluation. Real-time acquisition of bus passenger flow information is an indispensable part for bus intellectualization. The method of passenger flow statistics in bus video monitoring scene based on deep convolution neural network can provide rich information for passenger flow statistics. In order to adapt to the real scenario of mobile and embedded devices on buses, and to consider the bandwidth limitation, this paper uses a lightweight network model M7, which is suitable for the vehicle system. Based on the classic network model tiny YOLO, the model is optimized by a depthwise separable convolution method. The optimized network model M7 reduces the number of parameters and improves the detection speed, while maintaining a low loss in detection accuracy. As such, the network model M7 is compressed and further optimized by removing redundant channels. The experimental results show that the detection speed of the network model target recognition after channel compression is 40%, which is faster than the precious channel compression on the premise of ensuring detection.

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

“…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. While further information on each of those domains will be incorporated into the discussion in successive paragraphs to draw a clearer picture, it should be noted first that additional application areas, albeit almost residually with only one or two related works identified, have emerged in the analysis: (i) robotics [81,94], a domain where vision represents one of the most important communication channels with the environment, and where object detection has traditionally shown to be critical for the perception, modeling, planning, and understanding of unknown terrains [94]; (ii) defense, where object detection constitutes a major factor for controlling UAVs [84] and detecting ships in radar images [86]; (iii) smart logistics, with two distinct but equally representative examples of the use of sensing technologies, one on embedded platforms (in situ detection and recognition of ships for more efficient port management) [83], and the second one on mobile devices (barcode detection) [99] and finally, (iv) human emotion recognition based on facial expression detection, as reported in [71].…”

“…Finally, to conclude this first analysis focused on the current landscape of objectdetection-based AmI applications for low-power devices, we add to the discussion the two application domains not covered yet from the group of five with greater representation in the study: healthcare [73,74,88,97,110] and the so-called smart cities [72,100,101,108]. In regard to healthcare, on-device detection techniques are shown to be effective in extending healthcare spaces beyond the traditional scenario of closed clinical environments, bringing the capabilities of (i) disease diagnosis [73,74], (ii) wound or injury zone delimitation [97] and (iii) patient monitoring and support [88], (available only in typically complex and expensive configurations until recently) to low-cost portable devices.…”

“…Detection solutions are conceived instead as enabling services for more sophisticated vision tasks, as shown in Table 1. In addition, as far as smart cities are concerned, detection mechanisms are exploited to provide support to more complex tasks as well, but, in this particular case, seeking more resource-efficient management in everyday services in urban areas, such as public transportation [101] and garbage collection [72,100,108].…”

“…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. 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].…”

“…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.…”