Top view multiple people tracking by detection using deep SORT and YOLOv3 with transfer learning: within 5G infrastructure

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Manual inspection of fruit surface defects is time consuming, involves labour cost, prone to human error and possess inconsistent classification standards in fruit sorting application. To solve this issue, an automatic fruit sorting algorithm using deep learning technique was proposed to identify surface defects namely splitting, pitting, and stem-end rot found in mandarin fruits. The fruit sorting algorithm consists of K-Medoids segmentation and Convolutional Neural Network (CNN) classification model. The grayscale images of mandarin fruit surface were captured from an image acquisition system built with Near Infrared (NIR) camera. A preprocessing median filter was applied to remove random noise. After preprocessing, segmentation was carried out using K-Medoids clustering to crop the fruit surface image from the background region. Different CNN models namely VGG-16, InceptionV3 and MobileNet were trained and tested with and without transfer learning approach using the cropped image dataset. After training, the cropped fruit surface image was given to CNN model for defect classification. The classification results of the above models improved significantly after implementing transfer learning method. The VGG-16 model achieved a maximum overall classification accuracy of 90% without transfer learning and 99.53% with transfer learning approach when compared with the InceptionV3 and MobileNet. Overall accuracy of MobileNet improved from 57% to 98% after transfer learning and also it takes minimum time for inference. Considering both the overall accuracy and inference time parameters with the transfer learning approach, the MobileNet is found to be the best model for mandarin fruit sorting application.

Plant diseases have a huge impact on agricultural productivity and global food security. Rapid Identification and timely control of these diseases are crucial in order to reduce yield loss. During recent years deep learning techniques have demonstrated very promising results with regard to automating the detection of plants diseases from leaf images. Transfer learning, one of the popular approaches of deep learning, has been extensively employed to leverage large scale pre trained models and adapt them to new tasks with limited data. This paper presents a study on the application of transfer learning in the detection of plant leaf conditions diseases. A transfer learning approach is proposed where a pre trained convolutional neural. The purpose of this is fine-tuning a CNN model on plant-leaf images for the classification of various diseases.

Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals

We attempt to connect the data from complementary views, i.e., top view from drone-mounted cameras in the air, and side view from wearable cameras on the ground. Collaborative analysis of such complementary-view data can facilitate to build the air-ground cooperative visual system for various kinds of applications. This is a very challenging problem due to the large view difference between top and side views. In this paper, we develop a new approach that can simultaneously handle three tasks: i) localizing the side-view camera in the top view; ii) estimating the view direction of the side-view camera; iii) detecting and associating the same subjects on the ground across the complementary views. Our main idea is to explore the spatial position layout of the subjects in two views. In particular, we propose a spatial-aware position representation method to embed the spatial-position distribution of the subjects in different views. We further design a cross-view video collaboration framework composed of a camera identification module and a subject association module to simultaneously perform the above three tasks. We collect a new synthetic dataset consisting of top-view and side-view video sequence pairs for performance evaluation and the experimental results show the effectiveness of the proposed method.

This paper presents a vehicle counting system using multi-class vehicle detection using YOLOX and multi-object tracking using ByteTrack. Counting is performed for each class of the vehicle including bus, car, motorcycle, pickup, truck, and van in a predefined region of interest (ROI). Our proposed system is designed to handle noisy and low contrast traffic videos of top and side view of the vehicles. In particular, side view videos show the occlusion of the two directional lanes which lead to vehicle occlusion problems. For object detection and classification, YOLOX shows promising mean average precision (mAP) approximately at 77.2 and 58.8 percent for top and side views, respectively, which outperforms YOLOv3 for both top and side view datasets. The counting results show that ByteTrack with YOLOX can handle vehicle occlusion which occurs in the side view videos. Counting the vehicles within an ROI can reduce fault detections for both top view and side view videos.

BERT is a pre-trained language model that achieves state-of-the-art performance on natural language processing (NLP) tasks. Once it was published, it quickly became one of the most popular models in the NLP field. The official recommended method for applying BERT to downstream tasks is fine-tuning. However, we argue that transfer learning is also a very practical approach to applying BERT, and this approach has its own advantages compared to fine-tuning. In this paper, we explore the advantages of the transfer learning approach through the method that uses transfer learning and fine-tuning approaches separately to apply BERT to the same eight GLUE benchmark tasks and compare the training time spent and model performance scores, such as accuracy or F1 score, of the two approaches. Finally, this paper finds that among all eight GLUE tasks and on small training sets, transfer learning saves 30% to 50% of the time compared to a fine-tuning approach to train the models on the data of downstream tasks and can achieve very similar performance. Besides, for the same amount of time, transfer learning can obtain higher performance scores than fine-tuning on larger training sets. In conclusion, on small training sets, transfer learning has a huge advantage in time consumption under the premise of approximate performance, and it also performs better on large training sets under the same length of time, compared to fine-tuning.

Voice disorders are among the most prevalent problems affecting a huge proportion of the population, with the majority of them resulting in communication issues and a deterioration in quality of life. Early detection leads to more effective treatment. Thus, better approaches for accurately diagnosing vocal abnormalities are essential. Machine learning techniques such as Convolutional Neural Networks (CNNs), Random Forests, and transfer learning approaches have all proven useful in this discipline. CNNs offer incredible audio signal processing capabilities, since they can convert raw voice data into spectrograms and automatically extract key properties. CNNs can detect minor patterns associated with numerous voice disorders, improving classification accuracy. Random Forest is a technique used in ensemble learning to enhance robustness and accuracy in classification tasks. The utility of this approach stems from its capacity to overcome existing data processing issues, making it a substantial contribution to the area. Thus, we propose the system with CNN and Random Forest as the classification models and obtained the 97.31% accuracy for transfer learning model.

Purpose Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers. Design/methodology/approach In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection. Findings By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively. Originality/value In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

The amount of malware infecting computers and other communication devices and migrating across the internet has greatly increased over the years. Numerous methods and procedures have been put forth up to this point to find and eliminate these hostile agents. However, a lot of malwares is still being developed, which can get past some cutting-edge malware detection algorithms, as new and automated malware production techniques emerge. Consequently, there is a need for the classification and detection of these antagonistic agents that have the potential to compromise the security of individuals, businesses, and a wide range of other digital assets. To develop a new improved approach for efficient zero-day malware detection, there is a compelling need to reduce bias and objectively assess these methods. This study focuses on investigating transfer learning strategies for malware picture classification, including AlexNet, VGG16, VGG19, GoogLeNet, and ResNet. Here, malware binaries are turned into grayscale images before being processed using models for classification based on transfer learning. As transfer learning uses pre-trained models, the primary objective is to save training time. In addition, an effective model for malware classification will be built in order to obtain performance that is unbiased.

Plant disease classification and prevention of spreading of the disease at earlier stages based on visual leaves symptoms and Pest recognition through deep learning-based image classification is in the forefront of research. To perform the investigation on Plant and pest classification, Transfer Learning (TL) approach is used on EfficientNet-V2. TL requires limited labelled data and shorter training time. However, the limitation of TL is the pre-trained model network’s topology is static and the knowledge acquired is detrimentally overwriting the old parameters. EfficientNet-V2 is a Convolutional Neural Network (CNN) model with significant high speed learning rates across variable sized datasets. The model employs a form of progressive learning mechanism which expands the network topology gradually over the course of training process improving the model’s learning capacity. This provides a better interpretability of the model’s understanding on the test domains. With these insights, our work investigates the effectiveness of EfficienetV2 model trained on a class imbalanced dataset for plant disease classification and pest recognition by means of combining TL and progressive learning approach. This Progressive Learning for TL (PL-TL) is used in our work consisting of 38 classes of PlantVillage dataset of crops and fruit species, 5 classes of cassava leaf diseases and another dataset with around 102 classes of crop pest images downloaded from popular dataset platforms, though it is not a benchmark dataset. To test the predictability rate of the model in classifying leaf diseases with similar visual symptoms, Mix-up data augmentation technique is used at the ratio of 1:4 on corn and tomato classes which has high probability of misinterpretation of disease classes. Also, the paper compares the TL approach performed on the above mentioned three types of data set using well established CNN based Inceptionv3, and Vision Transformer a non-CNN model. It clearly depicts that EfficientNetV2 has an outstanding performance of 99.5%, 97.5%, 80.1% on Cassava, PlantVillage and IP102 datasets respectively at a faster rate irrespective of the data size and class distribution as compared to Inception-V3 and ViT models.The performance metrics in terms of accuracy, precision, f1-score is also studied.

<p>Skin cancer is an example of the most dangerous disease. Early diagnosis of skin cancer can save many people’s lives. Manual classification methods are time-consuming and costly. Deep learning has been proposed for the automated classification of skin cancer. Although deep learning showed impressive performance in several medical imaging tasks, it requires a big number of images to achieve a good performance. The skin cancer classification task suffers from providing deep learning with sufficient data due to the expensive annotation process and required experts. One of the most used solutions is transfer learning of pre-trained models of the ImageNet dataset. However, the learned features of pre-trained models are different from skin cancer image features. To end this, we introduce a novel approach of transfer learning by training the pre-trained models of the ImageNet (VGG, GoogleNet, and ResNet50) on a large number of unlabelled skin cancer images, first. We then train them on a small number of labeled skin images. Our experimental results proved that the proposed method is efficient by achieving an accuracy of 84% with ResNet50 when directly trained with a small number of labeled skin and 93.7% when trained with the proposed approach.</p>

The leather industry plays a crucial role in the production of high-quality leather products to remain competitive in the market. Due to various reasons such as material handling and turning, leather defects can appear at different stages of the production process. This paper proposed deep learning techniques, specifically transfer learning, deep convolutional neural networks (D-CNN), and ensemble learning for automating leather defect detection and classification. The dataset was taken from the open source Kaggle. The dataset was preprocessed from the beginning. However, we performed Error Level Analysis (ELA) to detect possible changes or fraud in the image. We also used data augmentation techniques (resizing, rescaling, flipping, rotation, zooming, and contrasting) on the dataset to improve the model performance. This study investigated deep learning architectures for automatic leather defect detection, comparing them with seven deep convolutional neural networks (D-CNN) models and transfer learning architectures. Furthermore, we introduce and evaluate two novel ensemble models: MER built on transfer learning architecture (MobileNetV2, EfficientNetB0, and ResNet50) and AZL built on D-CNN architecture (AlexNet, ZfNet, and LeNet). A key contribution of this study is the demonstration of significant performance improvements achieved by strategically integrating squeeze and excitation and label smoothing techniques into transfer learning, D-CNN, and ensemble frameworks. Our proposed MER ensemble method achieves a state-of-the-art of 96.39% for leather defect detection. The proposed model was evaluated using performance metrics including precision, recall, F1-score, AUC, ROC, and Matthews Correlation Coefficient (MCC). Additionally, Grad-CAM was employed for explainable AI to visualize and interpret the model’s decision-making process. Comparative analysis against separate transfer learning and D-CNN models fails to establish the superior performance of our MER ensemble, which highlights the potential to significantly advance automated quality inspection in leather production. This study provides insights into evaluating the best deep learning techniques for leather defect classification, paving the way for more accurate, efficient, and reliable industrial applications. Proposes a robust deep learning-based framework for automated leather defect detection using transfer learning, deep CNNs, and two novel ensemble models (MER and AZL), enhanced with squeeze-and-excitation and label smoothing techniques. Achieves state-of-the-art performance with the proposed MER ensemble model, achieve 96.39% accuracy, and demonstrates superior results across precision, recall, F1-score, AUC–ROC, and MCC. Integrates Error Level Analysis (ELA), extensive data augmentation, and Grad-CAM–based explainable AI to ensure data reliability, improve generalization, and provide transparent visual interpretation of defect regions for industrial quality inspection.

BackgroundBanana (Musa spp.) is the most popular marketable fruit crop grown all over the world, and a dominant staple food in many developing countries. Worldwide, banana production is affected by numerous diseases and pests. Novel and rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency. As deep convolutional neural networks (DCNN) and transfer learning has been successfully applied in various fields, it has freshly moved in the domain of just-in-time crop disease detection. The aim of this research is to develop an AI-based banana disease and pest detection system using a DCNN to support banana farmers.ResultsLarge datasets of expert pre-screened banana disease and pest symptom/damage images were collected from various hotspots in Africa and Southern India. To build a detection model, we retrained three different convolutional neural network (CNN) architectures using a transfer learning approach. A total of six different models were developed from 18 different classes (disease by plant parts) using images collected from different parts of the banana plant. Our studies revealed ResNet50 and InceptionV2 based models performed better compared to MobileNetV1. These architectures represent the state-of-the-art results of banana diseases and pest detection with an accuracy of more than 90% in most of the models tested. These experimental results were comparable with other state-of-the-art models found in the literature. With a future view to run these detection capabilities on a mobile device, we evaluated the performance of SSD (single shot detector) MobileNetV1. Performance and validation metrics were also computed to measure the accuracy of different models in automated disease detection methods.ConclusionOur results showed that the DCNN was a robust and easily deployable strategy for digital banana disease and pest detection. Using a pre-trained disease recognition model, we were able to perform deep transfer learning (DTL) to produce a network that can make accurate predictions. This significant high success rate makes the model a useful early disease and pest detection tool, and this research could be further extended to develop a fully automated mobile app to help millions of banana farmers in developing countries.

Increase in usage of cosmetics, pollution, and radiations will always result in skin-related diseases. Moreover, skin cancer has also become a common disease. There are many features available to help in the process of identification of skin cancer. Deep learning algorithms are successfully applied to identify the skin cancer. Various transfer learning strategies can also be applied. This paper attempts to compare various transfer learning approaches and the degree of accuracy by using these models. The analysis can be made by training the system with the details obtained from images available in the database and the current image is then tested to find whether it is malignant or not. The dataset images obtained from Kaggle dataset have been used for this study. Pre-trained models like InceptionV3, ResNet50, and MobileNet are used along with the extracted pre-trained weights in this research work.KeywordsSkin cancerDeep learningMobileNetResNet50Prediction