Abstract
Computer vision techniques have become increasingly popular in recent times. One of the applications of computer vision is the detection of objects in an image. In an object detection system, a target object is detected, localized, and labeled on the basis of the class of object. Detection of objects in harsh environments, accuracy in the detected objects, and speed at which they are detected are some of the major challenges in computer vision technology. Underwater imaging has its own set of difficulties namely: reduced visibility; objects possessing highly deformed shapes (plastic bags); means of communication of the captured image for proper processing. In this work, development of a system to detect marine life and plastic wastes in underwater environments is proposed. Deep learning technique is applied on images for the detection and localization of the desired target objects. A table comprises of the parameters of machine learning such as accuracy, precision, recall, F1 score, misclassification rate, true positive rate, false positive rate, true negative rate, and false negative rate.
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References
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A.C. Berg, Ssd: single shot multibox detector, in European Conference on Computer Vision (ECCV), 2016, pp. 21–37. Understanding Real-Time SSD Multi Box Object Detection in Deep Learning [Online]. Available https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab. Accessed on 12.10.2019
B. Benjdira, T. Khursheed, A. Koubaa, A. Ammar, K. Ouni, Car detection using unmanned aerial vehicles: comparison between faster R-CNN and YOLOv3, in 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS) (Muscat, Oman, 2019), pp. 1–6I; S. Jacobs, C.P. Bean, Fine particles, thin films and exchange anisotropy, in Magnetism, vol. III, ed. by G.T. Rado, H. Suhl (Academic, New York, 1963), pp. 271–350
Z. Lu, J. Lu, Q. Ge, T. Zhan, Multi-object detection method based on YOLO and ResNet hybrid networks, in 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM) (Toyonaka, Japan, 2019), pp. 827–832
R. Girshick, Fast R-CNN. arXiv:1504.08083 (2015)
M. Manana, C. Tu, P.A. Owolawi, Preprocessed faster RCNN for vehicle detection, in 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC) (PlaineMagnien, 2018), pp. 1–4
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, A.C. Berg, Ssd: single shot multibox detector, in European Conference on Computer Vision (ECCV), 2016, pp. 21–37
Image Classification with Keras and Deep Learning [Online]. Available https://www.pyimagesearch.com/2017/12/11/image-classification-with-keras-and-deep-learning/. Accessed on 16.01.2020
Turning any CNN image classifier into an object detector with Keras, TensorFlow, and OpenCV [Online]. Available https://www.pyimagesearch.com/2020/06/22/turning-any-cnn-image-classifier-into-an-object-detector-with-keras-tensorflow-and-opencv/. Accessed on 15.01.2020
How to run tensorflow lite for object detection on Raspberry Pi [Online]. Available https://www.youtube.com/watch?v=aimSGOAUI8Y. Accessed on 10.01.2020
How To Train an Object Detection Classifier Using TensorFlow (GPU) on Windows 10 [Online]. Available https://www.youtube.com/watch?v=Rgpfk6eYxJA&t=1167s. Accessed on 10.01.2020
Deep Learning with Python, Tensorflow and Keras Neural Network for Image Classification [Online]. Available https://www.youtube.com/watch?v=qEyEijUDOCA. Accessed on 02.05.2020
N.K. Manaswi, N.K. Manaswi, S. John. Deep Learning with Applications Using Python (Apress, 2018)
Accelerated Training and Inference with the Tensorflow Object Detection API [Online]. Available https://ai.googleblog.com/2018/07/accelerated-training-and-inference-with.html. Accessed on 22.06.2020
A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv:1704.04861 (2017)
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, D. Kalenichenko, Quantization and training of neural networks for efficient integer-arithmetic-only inference, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 2704—2713
SSD object detection: single shot multibox detector for real-time processing [Online]. Available https://medium.com/@jonathan_hui/ssd-object-detection-single-shot-multibox-detector-for-real-time-processing-9bd8deac0e06. Accessed on 06.09.2020
Compressing Deep Neural Nets [Online]. Available https://machinethink.net/blog/compressing-deep-neural-nets/. Accessed on 16.06.2020
Keras ImageDataGenerator and Data Augmentation [Online]. Available https://www.pyimagesearch.com/2019/07/08/keras-imagedatagenerator-and-data-augmentation/. Accessed on 10.04.2020
Izuzuki [Online]. Available https://www.izuzuki.com/, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=32245503. Accessed on 01.12.2020
Izuzuki [Online]. Available CC BY-SA 3.0, https://creativecommons.org/licenses/by-sa/3.0, via Wikimedia Commons. Accessed on 01.12.2020
Leprecub at en.wikipedia, CC BY 3.0 [Online]. Available https://commons.wikimedia.org/w/index.php?curid=6656059. Accessed on 01.12.2020
Leprecub at en.wikipedia, CC BY 3.0 [Online]. Available https://creativecommons.org/licenses/by/3.0, via Wikimedia Commons
Tensorflow/ Models [Online]. Available https://github.com/tensorflow/models, Accessed on 01.12.2020
Tensorflow 1 Detection Model Zoo [Online]. Available https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md. Accessed on 01.12.2020
TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi [Online]. Available https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi. Accessed on 01.12.2020
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Hegde, R., Patel, S., Naik, R.G., Nayak, S.N., Shivaprakasha, K.S., Bhandarkar, R. (2021). Underwater Marine Life and Plastic Waste Detection Using Deep Learning and Raspberry Pi. In: Kalya, S., Kulkarni, M., Shivaprakasha, K.S. (eds) Advances in VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems. Lecture Notes in Electrical Engineering, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-16-0443-0_22
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