Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

Akshatha, K. R.; Karunakar, A. K.; Shenoy, Bhaskar; Pai, Abhilash K.; Nagaraj, Nikhil Hunjanal; Rohatgi, Sambhav Singh

doi:10.3390/electronics11071151

Cited by 33 publications

(23 citation statements)

References 53 publications

(54 reference statements)

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“…Consequently, Liu et al (2016) proposed a Single Shot MultiBox Detector (SSD). The SSD algorithm uses multi-scale features and default anchor boxes to detect the presence of multi-scale objects in the scene in a single step ( Akshatha et al, 2022 ). Its network structure is shown in Supplementary Figure 1B .…”

Section: Methodsmentioning

confidence: 99%

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

et al. 2022

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The soybean flower and the pod drop are important factors in soybean yield, and the use of computer vision techniques to obtain the phenotypes of flowers and pods in bulk, as well as in a quick and accurate manner, is a key aspect of the study of the soybean flower and pod drop rate (PDR). This paper compared a variety of deep learning algorithms for identifying and counting soybean flowers and pods, and found that the Faster R-CNN model had the best performance. Furthermore, the Faster R-CNN model was further improved and optimized based on the characteristics of soybean flowers and pods. The accuracy of the final model for identifying flowers and pods was increased to 94.36 and 91%, respectively. Afterward, a fusion model for soybean flower and pod recognition and counting was proposed based on the Faster R-CNN model, where the coefficient of determinationR2 between counts of soybean flowers and pods by the fusion model and manual counts reached 0.965 and 0.98, respectively. The above results show that the fusion model is a robust recognition and counting algorithm that can reduce labor intensity and improve efficiency. Its application will greatly facilitate the study of the variable patterns of soybean flowers and pods during the reproductive period. Finally, based on the fusion model, we explored the variable patterns of soybean flowers and pods during the reproductive period, the spatial distribution patterns of soybean flowers and pods, and soybean flower and pod drop patterns.

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Section: Methodsmentioning

confidence: 99%

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

et al. 2022

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“…Object detection methods can be divided into two categories: two stage and one stage. In the two-stage object detection, the objects are first localized and then classified, and the representative algorithms are R-CNN [11], Fast R-CNN, and R-FCN [12]. One-stage object detection regards object detection as a regression problem and performs localization and classification at the same time.…”

Section: Related Workmentioning

confidence: 99%

Research on Fingerprint Security Based on Improved Yolo Algorithm

2022

Mobile Information Systems

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The quantitative identification technology based on the statistical law of fingerprint features has become a new research difficulty and focus, and the automatic detection and classification of fingerprint features are the basis for realizing automatic fingerprint feature statistics. In this paper, a YOLO-based fingerprint feature detection method was proposed. First, a fingerprint feature dataset was established, which contained a total of 4,000 annotated fingerprint images; then, according to the characteristics of small size and dense distribution of fingerprint feature points, the YOLO network structure was improved, the original large target feature detection layer by 32-fold downsampling was deleted, and a new small feature fusion layer was added; the FPN, PAN, and SPP structures were used to achieve local and global feature extraction through multiple-scale fusion methods; finally, the SE channel attention mechanism module was added to effectively enhance the model robustness and detection ability of dense small objects. The experimental results show that compared with the improved FP-YOLO model of the original model, when the detection speed is basically unchanged, the mAP0.5 value is increased from 93.0% to 97.4%, and the weight is reduced by 3/4.

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“…Based on the results, we can see that the proposed methodology has higher classification accuracy values than other deep learning approaches in terms of recognition rate Recall, Precision, and F-Score. The achieved F-Score for the proposed technique is 95.34%, compared to 90.85% for faster R-CNN [16], 84.49% for YOLOv3 [17], 68.47% for DCNN [15], and 92.50% for SSD [14]. The proposed method's acquired Precision is quite similar to SSD [14]'s precision.…”

Section: Performance Metricsmentioning

confidence: 73%

“…To identify human targets in aerial view thermal pictures, Akshatha et al [14] suggested Faster R-CNN and single-shot multi-box detector (SSD) algorithms with various backbone networks. To achieve this, two common aerial thermal datasets with variously sized human objects ResNet50, Inception-v2, and MobileNet-v1 are taken into consideration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed approach operates nicely in our database, according to the results. [14], Faster R-CNN [16], DCNN [15], YOLOV3 [17], and the planned DenseNet on the RGB-D-T database in terms of Recall, Accuracy, F-Score, and Precision. Based on the results, we can see that the proposed methodology has higher classification accuracy values than other deep learning approaches in terms of recognition rate Recall, Precision, and F-Score.…”

Section: Performance Metricsmentioning

confidence: 99%

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Human Face Emotions Recognition from Thermal Images Using DenseNet

Prasad

Chandana

2023

Int. j. electr. comput. eng. syst. (Online)

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In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

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Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

Cited by 33 publications

References 53 publications

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning

Research on Fingerprint Security Based on Improved Yolo Algorithm

Human Face Emotions Recognition from Thermal Images Using DenseNet

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