A Real-Time Garbage Truck Supervision and Data Statistics Method Based on Object Detection

Abstract

Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.

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.…”