MKLM: a multiknowledge learning module for object detection in remote sensing images

Zhang, Sanxing; Song, Fei; Leí, Tao; Jiang, Ping; Liu, Gang

doi:10.1080/01431161.2022.2061316

Cited by 7 publications

(5 citation statements)

References 44 publications

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“…Hence the model proves to be highly efficient than the compared models in terms of speed, accuracy and cost. The inception V3 model can be integrated with object detection tools, supported by the IoT framework, to assist visually-challenged people [22].…”

Section: Discussionmentioning

confidence: 99%

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Devi¹,

Albraikan²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on these test images, the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed. To further improve the performance of the Inception v3 model, the thermal exchange optimization (TEO) algorithm is applied to tune the hyperparameters (number of epochs, batch size, and learning rate) showing the novelty of the work. Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer, hyperparameter optimizer, and factorization approach. Additionally, Inception V3 can handle images of different sizes. This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things (IoT)-based devices.

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Section: Discussionmentioning

confidence: 99%

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Devi¹,

Albraikan²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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“…We evaluated the performance of the proposed RAIH-Det on the DOTA-Plane dataset with comparisons to seven state-of-the-art rotated object and keypoint detectors: RetinaNet-O (OBB) [57], CenterNet [16], CentripetalNet [41], BBAVectors [20], CFC-Net [40], ReDet [27], and MKLM [21]. Table 6 reports the OBB results on the DOTA-Plane test dataset.…”

Section: Comparison With the State-of-the-art Modelsmentioning

confidence: 99%

“…Compared with the former, the width and height of the latter directly reflect the relative size of the target. Recently, deep learning-based algorithms have emerged as an effective strategy for object detection research, such as the well-known YOLO series [10][11][12], single-shot multibox detector (SSD) [13], RetinaNet [14], CornerNet [15], CenterNet [16], IR R-CNN [17], rotation-sensitive regression for oriented scene text detection (RRD) [18], ROI Transformer [19], box boundary-aware vectors (BBAVectors) [20], and multiknowledge learning module (MKLM) [21], which have significantly improved detection accuracy. In nature scenes, most existing target head detection methods also directly treat head detection as a specific form of object detection.…”

Section: Introductionmentioning

confidence: 99%

RAIH-Det: An End-to-End Rotated Aircraft and Aircraft Head Detector Based on ConvNeXt and Cyclical Focal Loss in Optical Remote Sensing Images

Song

Leí

et al. 2023

Remote Sensing

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In airport ground-traffic surveillance systems, the detection of an aircraft and its head (AIH) is an important task in aircraft trajectory judgment. However, accurately detecting an AIH in high-resolution optical remote sensing images is a challenging task due to the difficulty in effectively modeling the features of aircraft objects, such as changes in appearance, large-scale differences, complex compositions, and cluttered background. In this paper, we propose an end-to-end rotated aircraft and aircraft head detector (RAIH-Det) based on ConvNeXt-T (Tiny) and cyclical local loss. Firstly, a new U-shaped network based on ConvNeXt-T with the same performance as the Local Vision Transformer (e.g., Swin Transformer) is presented to assess the relationships among aircraft in the spatial domain. Then, in order to enhance the sharing of more mutual information, the extended BBAVectors with six vectors captures the oriented bounding box (OBB) of the aircraft in any direction, which can assist in head keypoint detection by exploiting the relationship between the local and overall structural information of aircraft. Simultaneously, variant cyclical focal loss is adopted to regress the heatmap location of keypoints on the aircraft head to focus on more reliable samples. Furthermore, to perform a study on AIH detection and simplify aircraft head detection, the OBBs of the “plane” category in the DOTA-v1.5 dataset and the corresponding head keypoints annotated by our volunteers were integrated into a new dataset called DOTA-Plane. Compared with other state-of-the-art rotated object and keypoint detectors, RAIH-Det, as evaluated on DOTA-Plane, offered superior performance.

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“…Compared to traditional machine learning methods, deep learning models have the ability to automatically learn image features during the detection of SDTs ( Farias et al., 2018 ). They can also synthesize contextual information and semantic relationships within the image, enhancing detection accuracy ( Lei et al., 2021 ; Zhang et al., 2022 ). The end-to-end training approach simplifies the dead trees detection system and has the potential to improve overall performance and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery

Zhou,

Wu,

et al. 2024

Front. Plant Sci.

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Detecting and localizing standing dead trees (SDTs) is crucial for effective forest management and conservation. Due to challenges posed by mountainous terrain and road conditions, conducting a swift and comprehensive survey of SDTs through traditional manual inventory methods is considerably difficult. In recent years, advancements in deep learning and remote sensing technology have facilitated real-time and efficient detection of dead trees. Nevertheless, challenges persist in identifying individual dead trees in airborne remote sensing images, attributed to factors such as small target size, mutual occlusion and complex backgrounds. These aspects collectively contribute to the increased difficulty of detecting dead trees at a single-tree scale. To address this issue, the paper introduces an improved You Only Look Once version 7 (YOLOv7) model that incorporates the Simple Parameter-Free Attention Module (SimAM), an unparameterized attention mechanism. This improvement aims to enhance the network’s feature extraction capabilities and increase the model’s sensitivity to small target dead trees. To validate the superiority of SimAM_YOLOv7, we compared it with four widely adopted attention mechanisms. Additionally, a method to enhance model robustness is presented, involving the replacement of the Complete Intersection over Union (CIoU) loss in the original YOLOv7 model with the Wise-IoU (WIoU) loss function. Following these, we evaluated detection accuracy using a self-developed dataset of SDTs in forests. The results indicate that the improved YOLOv7 model can effectively identify dead trees in airborne remote sensing images, achieving precision, recall and mAP@0.5 values of 94.31%, 93.13% and 98.03%, respectively. These values are 3.67%, 2.28% and 1.56% higher than those of the original YOLOv7 model. This improvement model provides a convenient solution for forest management.

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MKLM: a multiknowledge learning module for object detection in remote sensing images

Cited by 7 publications

References 44 publications

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

RAIH-Det: An End-to-End Rotated Aircraft and Aircraft Head Detector Based on ConvNeXt and Cyclical Focal Loss in Optical Remote Sensing Images

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery

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