176 citations · 397 across the 20 of their papers we have counts for
18 papers · 1 filter
How many labels do you need? A decision framework for cross-habitat marine species recognition
Alzayat Saleh, Mostafa Rahimi Azghadi
Automated image recognition is increasingly used to scale ecological monitoring beyond manual annotation, yet ecologists lack evidence-based guidance on how much labelling effort r…
Multi-label Instance-level Generalised Visual Grounding in Agriculture
Mohammadreza Haghighat, Alzayat Saleh, Mostafa Rahimi Azghadi
Understanding field imagery such as detecting plants and distinguishing individual crop and weed instances is a central challenge in precision agriculture. Despite progress in visi…
Weed Detection in Challenging Field Conditions: A Semi-Supervised Framework for Overcoming Shadow Bias and Data Scarcity
Alzayat Saleh, Shunsuke Hatano, Mostafa Rahimi Azghadi
The automated management of invasive weeds is critical for sustainable agriculture, yet the performance of deep learning models in real-world fields is often compromised by two fac…
A Practical Approach to Underwater Depth and Surface Normals Estimation
Alzayat Saleh, Melanie Olsen, Bouchra Senadji +1
Monocular Depth and Surface Normals Estimation (MDSNE) is crucial for tasks such as 3D reconstruction, autonomous navigation, and underwater exploration. Current methods rely eithe…
Semi-Supervised Weed Detection for Rapid Deployment and Enhanced Efficiency
Alzayat Saleh, Alex Olsen, Jake Wood +2
Weeds present a significant challenge in agriculture, causing yield loss and requiring expensive control measures. Automatic weed detection using computer vision and deep learning…
FieldNet: Efficient Real-Time Shadow Removal for Enhanced Vision in Field Robotics
Alzayat Saleh, Alex Olsen, Jake Wood +2
Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and locali…
WeedCLR: Weed Contrastive Learning through Visual Representations with Class-Optimized Loss in Long-Tailed Datasets
Alzayat Saleh, Alex Olsen, Jake Wood +2
Image classification is a crucial task in modern weed management and crop intervention technologies. However, the limited size, diversity, and balance of existing weed datasets hin…
Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization
Alzayat Saleh, Md Mehedi Hasan, Herman W Raadsma +3
Accurate weight estimation and morphometric analyses are useful in aquaculture for optimizing feeding, predicting harvest yields, identifying desirable traits for selective breedin…
Adaptive deep learning framework for robust unsupervised underwater image enhancement
Alzayat Saleh, Marcus Sheaves, Dean Jerry +1
One of the main challenges in deep learning-based underwater image enhancement is the limited availability of high-quality training data. Underwater images are difficult to capture…
A lightweight Transformer-based model for fish landmark detection
Alzayat Saleh, David Jones, Dean Jerry +1
Transformer-based models, such as the Vision Transformer (ViT), can outperform onvolutional Neural Networks (CNNs) in some vision tasks when there is sufficient training data. Howe…
How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach
Alzayat Saleh, Marcus Sheaves, Dean Jerry +1
Tracking fish movements and sizes of fish is crucial to understanding their ecology and behaviour. Knowing where fish migrate, how they interact with their environment, and how the…
Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey
Alzayat Saleh, Marcus Sheaves, Dean Jerry +1
Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their…
Overcoming Annotation Bottlenecks in Underwater Fish Segmentation: A Robust Self-Supervised Learning Approach
Alzayat Saleh, Marcus Sheaves, Dean Jerry +1
Accurate fish segmentation in underwater videos is challenging due to low visibility, variable lighting, and dynamic backgrounds, making fully-supervised methods that require manua…
Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey
Alzayat Saleh, Marcus Sheaves, Mostafa Rahimi Azghadi
Marine scientists use remote underwater video recording to survey fish species in their natural habitats. This helps them understand and predict how fish respond to climate change,…
Affinity LCFCN: Learning to Segment Fish with Weak Supervision
Issam Laradji, Alzayat Saleh, Pau Rodriguez +3
Aquaculture industries rely on the availability of accurate fish body measurements, e.g., length, width and mass. Manual methods that rely on physical tools like rulers are time an…
A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis
Alzayat Saleh, Issam H. Laradji, Dmitry A. Konovalov +3
Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown gre…
Automatic Weight Estimation of Harvested Fish from Images
Dmitry A. Konovalov, Alzayat Saleh, Dina B. Efremova +2
Approximately 2,500 weights and corresponding images of harvested Lates calcarifer (Asian seabass or barramundi) were collected at three different locations in Queensland, Australi…
Underwater Fish Detection with Weak Multi-Domain Supervision
Dmitry A. Konovalov, Alzayat Saleh, Michael Bradley +3
Given a sufficiently large training dataset, it is relatively easy to train a modern convolution neural network (CNN) as a required image classifier. However, for the task of fish…