Oil Spill Detection and Recognition Utilizing Faster R-CNN with Enhanced Mobilenetv2 Architecture
Keywords:
Contrast Limited Adaptive Histogram Equalization, Enhanced Mobilenetv2, Faster R-CNN, Oil Spill DetectionAbstract
Oil spills are a major hazard to the environment, animals, and ecosystems. The identification of oil spills in a timely and accurate manner is critical for successful mitigation and response operations. We offer a complete strategy to oil spill detection and identification in this study by incorporating modern computer vision techniques. First, to improve picture quality and minimize noise in the input data, we use a Non-Adaptive Threshold with Contrast Limited Adaptive Histogram Equalization (CLAHE). This preprocessing procedure increases overall picture quality, making subsequent analysis more trustworthy. Then, using a Fused UNet Segmentation model, we apply the power of deep learning to picture segmentation. This approach efficiently isolates oil spill sites from the backdrop, allowing for exact identification and study of polluted areas. We use a Convolutional Neural Network (CNN) based on the well-known AlexNet architecture to extract relevant features from segmented photos. This stage extracts discriminative features, which improves the model's capacity to differentiate between oil spill and non-spill locations. The combination of Faster R-CNN with Enhanced MobileNetV2 architecture is at the core of our suggested solution. This hybrid approach delivers not only real-time processing but also cutting-edge performance in object identification tasks. We allow our model to identify and characterize oil spills correctly and effectively by training it on a dataset that includes both synthetic and real-world oil spill photos. To deliver a comprehensive solution for oil spill detection and identification, we integrate cutting-edge picture enhancement, segmentation, feature extraction, and object detection approaches. Experiment findings show that the system is excellent at detecting oil spills in a variety of environmental situations, allowing for faster reaction and mitigation measures to safeguard our valuable ecosystems. By employing advanced computer vision techniques, our system aligns with SDG 6 (Clean Water and Sanitation) by safeguarding water resources through accurate detection of oil spills. With a focus on SDG 14 (Life below Water), our technology aids in the preservation of marine ecosystems by minimizing the impact of oil spills on aquatic life.
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