Deep Learning – Object Detection
Real-time Object Detection for Lithium Battery
Objective
Lithium battery includes harmful metals (lead, mercury, etc.). Thus, collecting eco-friendly resources and managing hazardous materials are required during the discharge process. We propose a deep learning-based pipeline to select products containing the lithium battery among waste electrical and electronic products on a conveyor belt.
Data
Related Work
Related work: This study aims at the problem that mining conveyor belts are easily damaged under severe working conditions, based on the reclassification and definition of conveyor belt damage types. Conveyor belt damage is detected by the improved Yolov3 algorithm, which considers the impact of model scaling on the detection results [Zhang et al., 2021].
Proposed Method
We trained Electronic and Electronical data based on YOLOv4 with CSPDarknet53 as the backbone and made the pipeline for detecting products containing the lithium battery on a conveyor belt. When waste electrical and electronic products move on a conveyor belt, a line scan camera detects the product, if the product is a lithium battery product. Then, the network signal the reject device to select the product.
TRACER: Extreme Attention guided Salient Object Tracing Network
Objective
Salient object detection (SOD) aims to detect the objects that exhibit the most visual distinctiveness in an image. Existing approaches improved SOD performance; however, they are incapable of simultaneously achieving performance and computational efficiency. This study proposes an extreme attention-guided salient object tracing network called TRACER to address the inefficiencies in existing approaches.
Data
We performed the evaluation on five benchmark datasets: DUTS, DUT-OMRON, ECSSD, HKU-IS, and PASCAL-S.
Related Work
EGNet explicitly models complementary salient object information and salient edge information within the network to preserve the salient object boundaries.
PPA loss proposed in F3Net doesn’t treat pixels equally, which can synthesize the local structure information of a pixel to guide the network to focus more on local details.
Proposed Method
TRACER, which comprises an efficient backbone encoder (EfficientNet) along with attention-guided salient object tracing modules (masked edge, union, and object attention modules). Further, we propose an adaptive pixel intensity (API) loss function to deal with the relatively important pixels unlike conventional loss functions which treat all pixels equally.
TRACER achieved a state-of-the-art performance and computational efficiency in all the evaluation metrics when compared with the previous 13 methods on the five benchmark datasets. In particular, TE2 showed relatively similar performance compared to LDF, which was the previous outstanding method; however, TE2 required 2.3× fewer learning parameters and was 6× faster than LDF.
Study on the real-time object detection approach for end-of-lifebattery-powered electronics in the waste of electrical and electronic equipment recycling process
Objective
With the growing use of electrical and electronic equipment (EEE), managing end-of-life EEE has become critical. Thus, the demand for sorting and detaching batteries from EEE in real time has increased. In this study, we investigated real-time object detection for sorting EEE, which uses batteries, among numerous EEEs.
Data
Assuming actual WEEE recycling processes, to design deep learning to detect the battery-powered EEE products among various WEEE products, Images were collected from a total of 37 EEE products; where, a number of 12 EEE products, battery-powered type, and the remaining the number of 25 EEE products were non-battery type.
As a result of image collection for a total of 37 products, 21,218 and 5,184 images were collected through crowd source and open-source methods, respectively; a total 26,402 images were used for deep learning analysis.
Related Work
Fast RCNN [2] is a two-stage object detection method.
YOLO [3] proposed a one-stage object detection model.
[3] Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Proposed Method
Real-time OD has three components: the backbone, neck, and head. The backbone transforms an input image into a feature map to extract prominent information. The neck refines and reconfigures representation by combining feature maps obtained from different backbone levels. With this information, the head performs predictions, including classification and localization of an object.
Based on the above process, we seek the optimal object detection method for detecting battery-powered EEE products. To achieve it, we investigate the object detection types, backbone types, and learning strategies.
We compared single-stage detectors with two-stage detectors to search for the optimal network for detecting battery-powered EEE products. The Faster-RCNN performed better when the mAP was 0.50–0.95 of the total classes, but YOLO was 20 times faster than the two-stage algorithm in the measurement of the inference time while achieving a mAP score of 0.50–0.95, near that with Faster RCNN. Undoubtedly, this experiment helped in selecting a single-stage model to fit the domain problem of real-time electronic equipment detection. Additionally, transfer learning is effective for performance improvement.
To search for the optimal backbone, we conducted comprehensive experiments with 10 backbones. Among the 10 backbones, CSPDarknet53 performed best. Although EfficientNet and MobileNet focused on the efficiency and lightness of the network, CSPDarknet53 achieved a speed of 9.1 ms and a faster inference time, with a difference of 5.6 ms to 13 ms from the other backbones when it came to containing more or fewer parameters than the other backbones.
