Small object detection method with shallow feature fusion network for chip surface defect detection

Huang, Haixin; Tang, Xueduo; Wen, Feng; Jin, Xin

doi:10.1038/s41598-022-07654-x

Cited by 31 publications

(20 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For large models, the stochastic gradient(SGD) optimizer is preferred with a Binary Cross Entropy (BCE) loss 47 . Earlier this year, researchers have applied YOLO-based object detection methods for fault diagnostic 48 , 49 .…”

Section: Overview Of Objection Detection Modelsmentioning

confidence: 99%

End-to-end deep learning framework for printed circuit board manufacturing defect classification

Bhattacharya

Cloutier

2022

Sci Rep

View full text Add to dashboard Cite

We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model architecture and compare with the current state-of-the-art using the same PCB defect dataset. These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification. Results show that our method achieves a 98.1% mean average precision(mAP[IoU = 0.5]) on the test samples using low-resolution images. This is 3.2% better than the state-of-the-art using low-resolution images (YOLO V5m) and 1.4% better than the state-of-the-art using high-resolution images (FRCNN-ResNet FPN). While achieving better accuracies, our model also requires roughly 3× fewer model parameters (7.02M) compared with the state-of-the-art FRCNN-ResNet FPN (23.59M) and YOLO V5m (20.08M). In most cases, the major bottleneck of the PCB manufacturing chain is quality control, reliability testing and manual rework of defective PCBs. Based on the initial results, we firmly believe that implementing this model on a PCB manufacturing line could significantly increase the production yield and throughput, while dramatically reducing manufacturing costs.

show abstract

Section: Overview Of Objection Detection Modelsmentioning

confidence: 99%

End-to-end deep learning framework for printed circuit board manufacturing defect classification

Bhattacharya

Cloutier

2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Compared with YOLO v2, YOLO v3 provides multi-scale features. YOLO v4 was developed for small object detection [ 24 ]. Since the size of CSF lies in a certain range, we did not have the problem of detecting troublesome small objects and chose the YOLO v3 for the current study.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Measurement of Spinal Cerebrospinal Fluid by Cascade Artificial Intelligence Models in Patients with Spontaneous Intracranial Hypotension

Chai

Chen

et al. 2022

Biomedicines

View full text Add to dashboard Cite

Cerebrospinal fluid (CSF) hypovolemia is the core of spontaneous intracranial hypotension (SIH). More than 1000 magnetic resonance myelography (MRM) images are required to evaluate each subject. An effective spinal CSF quantification method is needed. In this study, we proposed a cascade artificial intelligence (AI) model to automatically segment spinal CSF. From January 2014 to December 2019, patients with SIH and 12 healthy volunteers (HVs) were recruited. We evaluated the performance of AI models which combined object detection (YOLO v3) and semantic segmentation (U-net or U-net++). The network of performance was evaluated using intersection over union (IoU). The best AI model was used to quantify spinal CSF in patients. We obtained 25,603 slices of MRM images from 13 patients and 12 HVs. We divided the images into training, validation, and test datasets with a ratio of 4:1:5. The IoU of Cascade YOLO v3 plus U-net++ (0.9374) was the highest. Applying YOLO v3 plus U-net++ to another 13 SIH patients showed a significant decrease in the volume of spinal CSF measured (59.32 ± 10.94 mL) at disease onset compared to during their recovery stage (70.61 ± 15.31 mL). The cascade AI model provided a satisfactory performance with regard to the fully automatic segmentation of spinal CSF from MRM images. The spinal CSF volume obtained through its measurements could reflect a patient’s clinical status.

show abstract

“…Improved Network Based on YOLO_v3. The shallow feature layer [24] of the neural network (close to the input layer) extracts low-level features. Low-level features are generalized and easy to express such as texture, color, and edges.…”

Section: Improved Ideas Based On Yolo_v3mentioning

confidence: 99%

Platelet Detection Based on Improved YOLO_v3

Liu

Ren

et al. 2022

Cyborg Bionic Syst

View full text Add to dashboard Cite

Platelet detection and counting play a greatly significant role in medical field, especially in routine blood tests which can be used to judge blood status and diagnose related diseases. Therefore, platelet detection is valuable for diagnosing related blood diseases such as liver-related diseases. Blood analyzers and visual microscope counting were widely used for platelet detection, but the experimental procedure took nearly 20 minutes and can only be performed by a professional doctor. In recent years, technological breakthroughs in artificial intelligence have made it possible to detect red blood cells through deep learning methods. However, due to the inaccessibility of platelet datasets and the small size of platelets, deep learning-based platelet detection studies are almost nonexistent. In this paper, we carried out experiments for platelet detection based on commonly used object detection models, such as Single Shot Multibox Detector (SSD), RetinaNet, Faster_rcnn, and You Only Look Once_v3 (YOLO_v3). Compared with the other three models, YOLO_v3 can detect platelets more effectively. And we proposed three ideas for improvement based on YOLO_v3. Our study demonstrated that YOLO_v3 can be adopted for platelet detection accurately and in real time. We also implemented YOLO_v3 with multiscale fusion, YOLO_v3 with anchor box clustering, and YOLO_v3 with match parameter on our self-created dataset and, respectively, achieved 1.8% higher average precision (AP), 2.38% higher AP, and 2.05% higher AP than YOLO_v3. The comprehensive experiments revealed that YOLO_v3 with the improved ideas performs better in platelet detection than YOLO_v3.

show abstract

Small object detection method with shallow feature fusion network for chip surface defect detection

Cited by 31 publications

References 24 publications

End-to-end deep learning framework for printed circuit board manufacturing defect classification

End-to-end deep learning framework for printed circuit board manufacturing defect classification

Quantitative Measurement of Spinal Cerebrospinal Fluid by Cascade Artificial Intelligence Models in Patients with Spontaneous Intracranial Hypotension

Platelet Detection Based on Improved YOLO_v3

Contact Info

Product

Resources

About