https://www.techscience.com/iasc/v28n2/42061/pdf

Tomato Leaf Disease Identification and Detection Based on Deep Convolutional Neural Network

Object tracking and detection techniques under GANN threats: A systemic review

Contemporary deep learning multi-scale deblurring models suffer from many issues: (I) They perform poorly on non-uniformly blurred images/videos; (II) Simply increasing the model depth with finer-scale levels cannot improve deblurring; (III) Individual RGB frames contain a limited motion information for deblurring; (IV) Previous models have a limited robustness to spatial transformations and noise. Below, we propose several mechanisms based on the multi-patch network to address the above issues: (I) We present a novel self-supervised event-guided deep hierarchical Multi-patch Network (MPN) to deal with blurry images and videos via fine-to-coarse hierarchical localized representations; (II) We propose a novel stacked pipeline, StackMPN, to improve the deblurring performance under the increased network depth; (III) We propose an event-guided architecture to exploit motion cues contained in videos to tackle complex blur in videos; (IV) We propose a novel self-supervised step to expose the model to random transformations (rotations, scale changes), and make it robust to Gaussian noises. Our MPN achieves the state of the art on the GoPro and VideoDeblur datasets with a 40 $$\times $$ faster runtime compared to current multi-scale methods. With 30 ms to process an image at 1280 $$\times $$ 720 resolution, it is the first real-time deep motion deblurring model for 720p images at 30 fps. For StackMPN, we obtain significant improvements over 1.2 dB on the GoPro dataset by increasing the network depth. Utilizing the event information and self-supervision further boost results to 33.83 dB. 

/pdf/event-guided-multi-patch-network-with-self-supervision-for-207dfhse.pdf

Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring

Deep learning is one of the artificial intelligence approaches that has recently become popular for minimizing human error. Deep learning techniques have the ability to successfully detect and interpret with the use of large amounts of data in many areas. Especially, the rapid increase in labeled data accumulated in the field of image processing has made it necessary to turn to deep learning algorithms. With the increasing data in these areas, deep learning methods are used to separate useful information from big data and to give meaning to text, images and audio files. In recent years, there has been an increase in the studies conducted in the field of object detection and object tracking. If there is an object to be followed after detection and analysis on non-stationary images such as videos, it is more difficult to extract meaningful information. In such cases, the use of deep learning algorithms enables image processing problems to be solved easily. The aim of this study is to examine the applications of deep learning and object detection and tracking, to explain the latest developments, to help researchers who will work in this field by giving information about popular libraries, data sets, algorithms.

/pdf/derin-ogrenme-teknikleri-ile-nesne-tespiti-ve-takibi-uzerine-11ygxwop16.pdf

Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme

Small target detection has been widely used in applications that are relevant to everyday life and have many real-time requirements, such as road patrols and security surveillance. Although object detection methods based on deep learning have achieved great success in recent years, they are not effective in small target detection. In order to solve the problem of low recognition rate caused by factors such as low resolution of UAV viewpoint images and little valid information, this paper proposes an improved algorithm based on the YOLOv5s model, called YOLOv5s-pp. First, to better suppress interference from complex backgrounds and negative samples in images, we add a CA attention module, which can better focus on task-specific important channels while weakening the influence of irrelevant channels. Secondly, we improve the forward propagation and generalisation of the network using the Meta-ACON activation function, which adaptively learns to adjust the degree of linearity or nonlinearity of the activation function based on the input data. Again, the SPD Conv module is incorporated into the network model to address the problems of reduced learning efficiency and loss of fine-grained information due to cross-layer convolution in the model. Finally, the detection head is improved by using smaller, smaller-target detection heads to reduce missed detections. We evaluated the algorithm on the VisDrone2019-DET and UAVDT datasets and compared it with other state-of-the-art algorithms. Compared to YOLOv5s, mAP@.5 improved by 7.4% and 6.5% on the VisDrone2019-DET and UAVDT datasets, respectively, and compared to YOLOv8s, mAP@.5 improved by 0.8% and 2.1%, respectively. For improving the performance of the UAV-side small target detection algorithm, it will help to enhance the reliability and safety of UAVs in critical missions such as military reconnaissance, road patrol and security surveillance.

Unmanned Aerial Vehicle Perspective Small Target Recognition Algorithm Based on Improved YOLOv5

https://www.techscience.com/jai/v2n2/39515/pdf

A Review of Object Detectors in Deep Learning

Artificial intelligence has been widely studied on solving intelligent surveillance analysis and security problems in recent years. Although many multimedia security approaches have been proposed by using deep learning network model, there are still some challenges on their performances which deserve in-depth research. On the one hand, high computational complexity of current deep learning methods makes it hard to be applied to real-time scenario. On the other hand, it is difficult to obtain the specific features of a video by fine-tuning the network online with the object state of the first frame, which fails to capture rich appearance variations of the object. To solve above two issues, in this paper, an effective object tracking method with learning attention is proposed to achieve the object localization and reduce the training time in adversarial learning framework. First, a prediction network is designed to track the object in video sequences. The object positions of the first ten frames are employed to fine-tune prediction network, which can fully mine a specific features of an object. Second, the prediction network is integrated into the generative adversarial network framework, which randomly generates masks to capture object appearance variations via adaptively dropout input features. Third, we present a spatial attention mechanism to improve the tracking performance. The proposed network can identify the mask that maintains the most robust features of the objects over a long temporal span. Extensive experiments on two large-scale benchmarks demonstrate that the proposed algorithm performs favorably against state-of-the-art methods.

/pdf/learning-attention-for-object-tracking-with-adversarial-v0o9h40yy7.pdf

Learning Attention for Object Tracking with Adversarial Learning Network

In this paper, we aim to propose a facial expression recognition algorithm by utilizing the spatial attention mechanism. We adopt 3 basic CNN models, including AlexNet, VGGNet and ResNet for comparison. Then we add the attention module, i.e., SENet to VGGNet and ResNet for futher feature enhancement. Our results on FER2013 show the effectiveness of our method. Our VGG+SENet achieves 65.0% accuracy and our ResNet+SENet achieves 66.8% accuracy. Both method with attention can get an obvious performance promotion, which validates the effectiveness of attention mechanism.

Facial Expression Recognition with Squeeze-and-Excitation Network

Object detection is one of the most fundamental tasks toward image content understanding. Although numerous algorithms have been proposed, implementing effective and efficient object detection is still very challenging for now. To solve the challenges of small and multi-scale objects, we propose a hierarchical feature fusion and reconstruction method called feature fusion and reconstruction single shot detector (FFR-SSD). We first present a multi-scale visual attention model, which incorporates the channel-wise and space-wise information into the multi-branch feature enhancement module to improve the feature representation capacities. Second, a hierarchical feature map weighing mechanism is developed to fuse multi-layer feature maps, which contributes to describing the intact objects for the subsequent module. Third, we present an effective feature map reconstruction module to encourage the model to focus on pivotal information. Therefore, the overall contour information is preserved for the shallow enhanced response map and the semantic feature information is enriched for the deep enhanced feature map. Numerous experiments on two public benchmark datasets show that the proposed method achieves significant improvements over the state of the arts. 

https://www.researchsquare.com/article/rs-1774310/latest.pdf

FFR-SSD: feature fusion and reconstruction single shot detector for multi-scale object detection

Imdet: Injecting More Supervision to Centernet-Like Object Detection

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