Retracted: Object detection and recognition using deep learning‐based techniques

Sharma, Pallavi; Gupta, Surbhi; Vyas, Sonali; Shabaz, Mohammad

doi:10.1049/cmu2.12513

Cited by 5 publications

(4 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the second category presents single-stage frameworks that sacrifice some accuracy for enhanced speed. Methods like the You Only Look Once (YOLO) series [58] (including YOLOv5 [59], YOLOv6 [60], YOLOv7 [61], YOLOv8 [62], and YOLO-NAS [63]), Single-Shot MultiBox Detector (SSD) [64], and OverFeat [65] fall into this category. Single-stage frameworks bypass the region proposal generation stage, directly predicting class probabilities and bounding box offsets from entire images simultaneously.…”

Section: Deep Learning Strategies For Occlusion Handlingmentioning

confidence: 99%

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Ouardirhi,

Mahmoudi,

Zbakh

2024

Electronics

View full text Add to dashboard Cite

Smart video surveillance systems (SVSs) have garnered significant attention for their autonomous monitoring capabilities, encompassing automated detection, tracking, analysis, and decision making within complex environments, with minimal human intervention. In this context, object detection is a fundamental task in SVS. However, many current approaches often overlook occlusion by nearby objects, posing challenges to real-world SVS applications. To address this crucial issue, this paper presents a comprehensive comparative analysis of occlusion-handling techniques tailored for object detection. The review outlines the pretext tasks common to both domains and explores various architectural solutions to combat occlusion. Unlike prior studies that primarily focus on a single dataset, our analysis spans multiple benchmark datasets, providing a thorough assessment of various object detection methods. By extending the evaluation to datasets beyond the KITTI benchmark, this study offers a more holistic understanding of each approach’s strengths and limitations. Additionally, we delve into persistent challenges in existing occlusion-handling approaches and emphasize the need for innovative strategies and future research directions to drive substantial progress in this field.

show abstract

Section: Deep Learning Strategies For Occlusion Handlingmentioning

confidence: 99%

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Ouardirhi,

Mahmoudi,

Zbakh

2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Eventually, for optimal parameter adjustment, hybrid salp swarm optimization (HSSO) method was employed. Sharma et al [15] define many DL methods and their features for object recognition in videos and pictures. Other object detection applications, namely autonomous driving, face identification, and pedestrian detection, are explored.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Gogineni

Bhuyan

Lydia³

2023

Preprint

View full text Add to dashboard Cite

Pedestrian detection using object detection and deep learning has been found to be effective method for identifying pedestrians in video frames or images accurately. It is more commonly used in many real-time applications, such as security observing systems, autonomous driving systems, and robotics. The combination of deep learning techniques and object detection algorithms allows efficient and robust detection of pedestrians in several real-time scenarios. But it is necessary to improve the detection efficacy for complex environments such as cases with worse visibility due to weather or daytime, crowd scenes, and rare pose samples. Continuous improvement and research in DL algorithms, dataset collection, and training models contribute to accelerating the robustness and accuracy of pedestrian detection systems. Therefore, this study designs a new marine predator algorithm with deep learning-based pedestrian detection and classification (MPADLB-PDC) technique. The objective of the MPADLB-PDC approach lies in the accurate recognition and classification of pedestrians. To achieve this, the MPADLB-PDC technique involves two major processes, namely object detection and classification. In the first stage, the MPADLBPDC technique uses an improved YOLOv7 object detector for the recognition of the objects in the frame. Next, in the second stage, the ensemble classifier comprises three classifiers such as deep feed-forward neural network (DFFNN), extreme learning machine (ELM), and long short-term memory (LSTM). To improve the recognition performance of the ensemble classifier, the MPA is used to optimally select the parameters related to it. The simulation outcome of the MPADLB-PDC system was validated on the pedestrian database, and the outcome can be studied interms of various aspects. The simulation values validated the better outcome of the MPADLB-PDC system compared to other approaches.

show abstract

“…CNNs employ a set of convolution and pooling operations to extract essential features from images. In our method, we pass each image and its corresponding annotations through a feature extractor algorithm, resulting in the generation of a feature map [41], [42]. For this purpose, we employ pre-trained networks trained on the ImageNet dataset to ensure efficient and effective extraction of image features.…”

Section: Features' Extraction Using the Shared Cnnmentioning

confidence: 99%

Classification and Localization of Arabic Handwritten Text in KHATT Dataset Based on Faster R-CNN

May Mowaffaq AL-Taee

2023

pst

View full text Add to dashboard Cite

Arabic handwriting recognition is an important research area in computer vision. Due to the complexity of the Arabic script, this is an arduous task. Several approaches have been proposed to address this challenge, including deep learning algorithms. Despite their efficiency, these approaches present some limitations such as the use of lexicon-driven models, the need of a lot of data for training and the huge computational cost. We propose, in this paper, two novel models based on the robust Faster Region-Convolution Neural Network (Faster R-CNN) to recognize Arabic handwritten sentences. The Faster R-CNN is commonly used to detect objects in images and depends on region proposal algorithms. These models use the pre-trained VGG 16 and ResNet50. Moreover, they use a soft non-maximum suppression strategy (Soft-NMS) in the post-processing stage instead of the traditional NMS to increase the detection rate of overlapping items. The models will be trained independently to identify words in the text lines and tested using sentences from the (KFUPM Handwritten Arabic TexT) KHATT dataset. It will be shown that the faster R-CNN models allow greater accuracy and effectiveness in handwriting recognition. They achieve accuracy rates of 99% and 98% for the two networks, VGG16 and ResNet50, respectively

show abstract

Retracted: Object detection and recognition using deep learning‐based techniques

Cited by 5 publications

References 52 publications

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Enhancing Object Detection in Smart Video Surveillance: A Survey of Occlusion-Handling Approaches

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Classification and Localization of Arabic Handwritten Text in KHATT Dataset Based on Faster R-CNN

Contact Info

Product

Resources

About