Computer Vision and Robotics

Table of Contents

1. FocusNet: A Pathogenetically Oriented Deep Learning Framework for Enhanced Diagnostics and Treatment of Fundus Pathologies

   R. Bhuvanya, A. Saravanan, V. Vanitha, K. P. Koushik, S. Heblin Bersilla, R. Bharani Rajan

   Abstract

   One of the biggest health issues across the globe is eye diseases causing irreversible vision loss or blindness. Vision loss can be prevented by early and accurate diagnosis with proper treatment. This paper investigates three distinct models: MobileNet, ResNet, and DenseNet, along with their respective variants (MobileNet V1, V2, ResNet-50, ResNet-101, DenseNet-121, and DenseNet-169). Additionally, a pixel-wise attention mechanism is integrated with all the selected models. According to the experimental results, all the models incorporated with attention mechanism yielded good results. The findings of our study demonstrate that FocusNet based on DenseNet169 architecture with an attention mechanism, achieved the highest accuracy of 95%, followed by DenseNet 121 with 93%, MobileNet v2 with 91% ResNet 101 with 73%, and ResNet 50 lagged with accuracy of 51%. These findings highlight the effectiveness of attention mechanism with deep learning models for reliable eye disease classification. Also this study underscores the potential of attention driven deep learning framework in diagnosing ophthalmic diseases.

2. Optimizing Tomato Disease Classification Using Deep Learning Ensemble Approach with Color Opponency Space

   Gurpreet Singh, Sandeep Sharma

   Abstract

   One of the most significant crops grown in India is the tomato. Many deep learning models have found widespread application in the precise categorization of various tomato diseases. The deep learning plant pathology models are based on the popular convolutional neural network architectures such as Inception v3, DenseNet121, and ResNet50. This paper aims to improve the prediction accuracy of these three neural networks by using them with the global color constancy approach known as color opponency space (COS), employing hue, saturation, and value. Furthermore, these three models are applied in combination approaches using ensemble learning techniques such as soft voting and weighted voting to find the best-performing combination in terms of accuracy. Inception v3 with COS, DenseNet121 without COS, and ResNet50 with COS are the recommended configurations. This combination achieves 97.74% accuracy, which is higher than any other combination of these three models. This approach demonstrates the potential of hybrid ensemble-CNN frameworks in elevating plant disease classification accuracy for real-world agricultural applications.

3. CNN-Autoencoder with Linear Regression-Based Image Analyzer for Detection of Defects in SQ59 Armatures

   Suraj Sunil Joshi, Devarshi Anil Mahajan, Atharva Deshmukh, Mukta Dinesh Deore, Pooja Mishra, Piyush Jadhav

   Abstract

   During the production of armatures, many types of defects in its structure arise, making effective quality assurance techniques necessary. We have proposed a CNN-Autoencoder with linear regression-based image analyzer for detecting defective armatures using a dataset of over 500 images. CNN was used for feature extraction and reducing data dimensions. An autoencoder will detect anomalies using the perfect armature pieces segregating the defective ones. Lastly, a linear regression layer has been developed for classification based on intra-variations in the image distribution. The features for the regression model were generated using various statistical techniques in which the intra-image variation and distribution was considered. The F1-score achieved was 80.01%.

4. Project Management with Tamper-Proof Evaluation System Using Blockchain and Secured Storage

   S. Sudharsana Saravanan, C. Swetha, S. Shravanthi, S. R. Sarvanthikha, U. Gayathri, N. Harini

   Abstract

   In the prevalent project management systems, the employee’s performance evaluation is a crucial component for preserving efficiency and growth. However, these systems often fail to provide precise, candid, and tamper-proof evaluations, which leads to wrongful analysis and irregularities. This paper proposes a blockchain-based tamper-proof evaluation system for project management, combined with protected storage using IPFS which will improve the credibility and safety of performance data. The system uses three key components through a private blockchain network for consistent data-recording, self-operating code evaluation tools including MOSS and Diffchecker for unbiased analysis, and IPFS-based decentralized storage for the data to be evaluated. This system focuses on improving data safety and is planned to reduce biased evaluation by 70–80% compared to traditional methods. It also includes a mechanism for managerial supervision which allows the managers to alter assessments in case the evaluation tool’s assessment has any error. Unlike the current systems, each and every modification is cryptographically signed, guaranteeing trackability while preventing unfairness. The blockchain layer is designed using a permissioned consensus mechanism, where only authenticated nodes participate in transaction validation and block propagation, ensuring robust access control and data integrity. Also, we use IPFS to access previous performance data while ensuring safety and independence from centered servers. To preserve authenticity, all the changes made by the manager are documented and forwarded to higher authorities for review. This regulated flexibility balances computerization with human judgement, ensuring fairness in performance assessment enhancing the impartiality of employee assessments. Moreover, the employees will get to be in a stress-free healthier environment and a more balanced workplace.

5. Edge-Optimized Hybrid Framework for Image Super-Resolution Using Deep Learning and Fuzzy Logic

   Ananya Vemula, Amit Kumar Bairwa

   Abstract

   Image super-resolution (SR) addresses critical needs in medical diagnostics and geo-spatial analysis by enhancing low-resolution imaging data. While deep learning methods like Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) achieve high visual quality through adversarial training, they exhibit limitations in preserving anatomically critical edges in MRI scans and topographic features in satellite imagery. This paper presents a novel three-stage architecture that combines the generative capabilities of ESRGANof ESRGAN with a fuzzy inference system for edge optimization.

   The framework demonstrates a 7.8% improvement in the peak signal-to-noise ratio and 12% higher edge preservation scores compared to baseline ESRGAN on the DIV2K benchmark. The hybrid approach enables an interpretable edge enhancement through 27 fuzzy rules that govern gradient map optimization, addressing key limitations of purely data-driven methods.

6. Backmatter