http://nopr.niscpr.res.in/handle/123456789/61353 <p>Special Issue Industry 4.0: A Way Forward for Self-reliance and Sustainability—Part II.</p> Fri, 09 Oct 2026 21:49:48 GMT 2026-10-09T21:49:48Z http://nopr.niscpr.res.in/handle/123456789/61369 Title: Hyper Parameter Optimization for Transfer Learning of ShuffleNetV2 with Edge Computing for Casting Defect Detection Authors: L V, Narasimha Prasad; Dokku, Durga Bhavani; Talasila, Sri Lakshmi; Tumuluru, Praveen Abstract: A casting defect is an expendable abnormality and the most undesirable thing in the metal casting process. In Casting Defect Detection, deep learning based on Convolution Neural Network (CNN) models has been widely used, but most of these models require a lot of processing power. This work proposes a low-power ShuffleNet V2-based Transfer Learning model for defect identification with low latency, easy upgrading, increased efficiency, and an automatic visual inspection system with edge computing. Initially, various image transformation techniques were used for data augmentation on casting datasets to test the model flexibility in diverse casting. Subsequently, a pre-trained lightweight ShuffleNetV2 model is adapted, and hyperparameters are fine-tuned to optimize the model. The work results in a lightweight, adaptive, and scalable model ideal for resource-constrained edge devices. Finally, the trained model can be used as an edge device on the NVIDIA Jetson Nano-kit to speed up detection. The measures of precision, recall, accuracy, and F1 score were utilized for model evaluation. According to the statistical measures, the model accuracy is 99.58%, precision is 100%, recall is 99%, and the F1-Score is 100 %. Page(s): 171-177 Wed, 01 Feb 2023 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/61369 2023-02-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61368 Title: Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry Authors: D, Shobha Rani; Burra, Lakshmi Ramani; G, Kalyani; B, Narendra Kumar Rao Abstract: Surface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on lowfrequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them. Page(s): 178-184 Wed, 01 Feb 2023 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/61368 2023-02-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61367 Title: Object Sub-Categorization and Common Framework Method using Iterative AdaBoost for Rapid Detection of Multiple Objects Authors: Rao, B Narendra Kumar; Ranjana, R; Challa, Nagendra Panini; Chakravarthi, S Sreenivasa; Vellingiri, J Abstract: Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques. Page(s): 185-191 Wed, 01 Feb 2023 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/61367 2023-02-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/61366 Title: Learning How to Detect Salient Objects in Nighttime Scenes Authors: Mu, Nan; Guo, Jinjia; Tang, Jinshan Abstract: The detection of salient objects in nighttime scene settings is an essential research issue in computer vision. None of the known approaches can accurately anticipate salient objects in the nighttime scenes. Due to the lack of visible light, spatial visual information cannot be accurately perceived by traditional and deep network models. This paper proposed a Mountain Basin Network (MBNet) to identify salient objects for distinguishing the pixel-level saliency of low-light images. To improve the objects localizations and pixel classification performances, the proposed model incorporated a High-Low Feature Aggregation Module (HLFA) to synchronize the information from a high-level branch (named Bal-Net) and a lowlevel branch (called Mol-Net) to fuse the global and local context, and a Hierarchical Supervision Module (HSM) was embedded to aid in obtaining accurate salient objects, particularly the small ones. In addition, a multi-supervised integration technique was explored to optimize the structure and borders of salient objects. In the meantime, to facilitate more investigation into nighttime scenes and assessment of visual saliency models, we created a new nighttime dataset consisting of thirteen categories and a total of one thousand low-light images. Our experimental results demonstrated that the suggested MBNet model outperforms seven current state-of-the-art methods for salient object detection in nighttime scenes. Page(s): 192-201 Wed, 01 Feb 2023 00:00:00 GMT http://nopr.niscpr.res.in/handle/123456789/61366 2023-02-01T00:00:00Z