Deep Convolutional Neural Networks Object Detector for Real-Time Waste Identification

Abstract

This paper presents an extensive research carried out for enhancing the performances of convolutional neural network (CNN) object detectors applied to municipal waste identification. In order to obtain an accurate and fast CNN architecture, several types of Single Shot Detectors (SSD) and Regional Proposal Networks (RPN) have been fine-tuned on the TrashNet database. The network with the best performances is executed on one autonomous robot system, which is able to collect detected waste from the ground based on the CNN feedback. For this type of application, a precise identification of municipal waste objects is very important. In order to develop a straightforward pipeline for waste detection, the paper focuses on boosting the performance of pre-trained CNN Object Detectors, in terms of precision, generalization, and detection speed, using different loss optimization methods, database augmentation, and asynchronous threading at inference time. The pipeline consists of data augmentation at the training time followed by CNN feature extraction and box predictor modules for localization and classification at different feature map sizes. The trained model is generated for inference afterwards. The experiments revealed better performances than all other Object Detectors trained on TrashNet or other garbage datasets with a precision of 97.63% accuracy for SSD and 95.76% accuracy for Faster R-CNN, respectively. In order to find the optimal higher and lower bounds of our learning rate where the network is actually learning, we trained our model for several epochs, updating the learning rate after each epoch, starting from 1 × 10−10 and decreasing it until reaching 1 × 10−1.

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].…”

Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste

“…Ref. [5] sought to detect and classify materials using a dataset called TrashNet and the MobileNet V2 and Inception V2 networks, achieving a 97% accuracy. Ref.…”

“…In the later study ( [5]) the elements are already in a photograph. In the later study, the R-CNN network was successful in detecting the garbage in the image.…”