An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle imagery

Atitallah, Safa Ben; Driss, Maha; Boulila, ‪Wadii; Koubâa, Anis; Atitallah, Nesrine; Ghézala, Henda Ben

doi:10.1016/j.procs.2021.08.059

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…By flattening the output layer, reducing its dimensions to one, and adding a sigmoid layer for classification along with a fully connected layer with 1,024 hidden units, with a ReLU activation function, as shown in Equation 3, and a dropout rate of 0.4, we were able to use Inception V3 by avoiding its overfitting. The neuronal weights of the classification layers are initialized with the algorithm described in ( 25 ), as shown in Equation 4.…”

Section: Methodsmentioning

confidence: 99%

Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16

Shanshan

Li²,

Wang³

et al. 2023

Front. Neurol.

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PurposeThis study aims to automatically classify color Doppler images into two categories for stroke risk prediction based on the carotid plaque. The first category is high-risk carotid vulnerable plaque, and the second is stable carotid plaque.MethodIn this research study, we used a deep learning framework based on transfer learning to classify color Doppler images into two categories: one is high-risk carotid vulnerable plaque, and the other is stable carotid plaque. The data were collected from the Second Affiliated Hospital of Fujian Medical University, including stable and vulnerable cases. A total of 87 patients with risk factors for atherosclerosis in our hospital were selected. We used 230 color Doppler ultrasound images for each category and further divided those into the training set and test set in a ratio of 70 and 30%, respectively. We have implemented Inception V3 and VGG-16 pre-trained models for this classification task.ResultsUsing the proposed framework, we implemented two transfer deep learning models: Inception V3 and VGG-16. We achieved the highest accuracy of 93.81% by using fine-tuned and adjusted hyperparameters according to our classification problem.ConclusionIn this research, we classified color Doppler ultrasound images into high-risk carotid vulnerable and stable carotid plaques. We fine-tuned pre-trained deep learning models to classify color Doppler ultrasound images according to our dataset. Our suggested framework helps prevent incorrect diagnoses caused by low image quality and individual experience, among other factors.

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Section: Methodsmentioning

confidence: 99%

Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16

Shanshan

Li²,

Wang³

et al. 2023

Front. Neurol.

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“…), were readily distinguished and classifiable within our images resulting from distinct reflectance signatures in NIR and RBG bands, the signature of Wright fishhook cacti was limited and less effective in distinguishing Wright fishhook cactus plants in both NIR and RGB images. In comparison, Atitallah et al [18] were able to detect larger cactus plants using sUASs whereas Cerrejon et al [19] found detection of small rare plants to be limiting. This weakness in our study is likely associated with Wright fishhook cactus morphological and surface properties including high densities of large clustered non-photosynthetic thorns, small short plant stature, dust accumulation across the plant surface, and small size of flowers in relation to the image resolution.…”

Section: Mission Planning and Flightsmentioning

confidence: 98%

A Practical Assessment of Using sUASs (Drones) to Detect and Quantify Wright Fishhook Cactus (Sclerocactus wrightiae L.D. Benson) Populations in Desert Grazinglands

Bates

Anderson

Johnson

et al. 2022

Land

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Obtaining accurate plant population estimates has been integral in listing, recovery, and delisting species under the U.S. Endangered Species Act of 1973 and for monitoring vegetation in response to livestock grazing. Obtaining accurate population estimates remains a daunting and labor-intensive task. Small unmanned aircraft systems (sUASs or drones) may provide an effective alternative to ground surveys for rare and endangered plants. The objective of our study was to evaluate the efficacy of sUASs (DJI Phantom 4 Pro) for surveying the Wright fishhook cactus (Sclerocactus wrightiae), a small (1–8 cm diameter) endangered species endemic to grazinglands in the southwest desert of Utah, USA. We assessed sUAS-based remotely sensed imagery to detect and count individual cacti compared to ground surveys and estimated optimal altitudes (10 m, 15 m, or 20 m) for collecting imagery. Our results demonstrated that low altitude flights provided the best detection rates (p < 0.001) and counts (p < 0.001) compared to 15 m and 20 m. We suggest that sUASs can effectively locate cactus within grazingland areas, but should be coupled with ground surveys for higher accuracy and reliability. We also acknowledge that these technologies may have limitations in effectively detecting small, low-growing individual plants such as the small and obscure fishhook cactus species.

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“…It is used in this study because it deals with a huge amount of data [18,20]. Additio nally, it has good performance and simplicity in processing and analyzing datasets significantly in image classification tasks [21,22]. The output results are mapped with the input data resulting a function from input to output [23,24].…”

Section: Introductionmentioning

confidence: 99%

Developing a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network

Saif

Muneer

Abdulrahman³

et al. 2022

TAPR

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The object of research is a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network. Within the recent few decades, fires are considered as one of the most serious disaster that occurs in many places around the world. The severity of fire incidents causes damages to buildings, infrastructures and properties. Resulting losses of human’s life and costs them a lot of losses. Thus, fire poses a great threat to us significantly; it is extremely dangerous for fire fighters. Fires can be resulted by materials such as rubber and chemical products. Other sources of fire are the short circuits on electrical devices and faults in power circuits. Additionally, overheating and overloading problems can be the cause of fire incidents. All these reasons lead to bad consequences when there is no immediate response to such problems. The advent of computer vision technology has played such a significant role for human life. Artificial intelligence field has improved the efficiency and behaviors of robotics beyond expectations. The interference of artificial intelligence made robotics act intelligently. For this reason, in this paper we presented a mobile robot based on deep learning to detect the fire source and determines its coordinate position then automatically moves toward the target and extinguish fire. Deep learning algorithms are the efficient ones for object detection applications. CNN model is one of the most common deep learning algorithms which have been used in the study for the fire detection. Due to the insufficient amount of datasets and large efforts required to build model from scratch. MobileNet V2 is one of the CNN models that support transfer learning technique. After training the model and testing it on 20 % of the used datasets the classification accuracy achieved up to 98.01 %. The motion repeatability of the robot has been implemented and tested resulting mean error 0.648 cm.

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An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle imagery

Cited by 6 publications

References 30 publications

Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16

Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16

A Practical Assessment of Using sUASs (Drones) to Detect and Quantify Wright Fishhook Cactus (Sclerocactus wrightiae L.D. Benson) Populations in Desert Grazinglands

Developing a prototype of fire detection and automatic extinguisher mobile robot based on convolutional neural network

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