Search

The proliferation of deep learning has driven research into deep learning‐based logo detection, which usually needs a large number of annotated data to train the model. However, due to the occasional appearance of new brands or the high cost of annotation, the number of training data is limited. Against this backdrop, the authors adapt the few‐shot object detection into logo detection, and thus present a cutting‐edge method called Double Classification Head (DCH) for Few‐Shot Logo Detection (DCH‐FSLogo), which aims at detecting the unseen logo classes using few annotated data. Unlike the traditional few‐shot detection, some logo objects are similar to their backgrounds and have diverse shapes as well. For this reason, the authors adopt balanced feature pyramid and deformable Region of Interest pooling in DCH‐FSLogo, this enhances the feature extraction capability and adapts to the different logo shapes. In addition, we introduce the DCH for few‐shot logo detection to detect logo objects using few annotated data. Specifically, we use an extra classification head for the base classes to ease the influence from the novel classes. The experimental results on four datasets, namely: FlickrLogos‐32, FoodLogoDet‐1500‐100, LogoDet‐3K‐100 and QMUL‐OpenLogo‐100, demonstrate that our method achieves better performance. [ABSTRACT FROM AUTHOR]

Logo detection is a technology that identifies logos in images and returns their locations. With logo detection technology, brands can check how often their logos are displayed on social media platforms and elsewhere online and how they appear. It has received a lot of attention for its wide applications across different sectors, such as brand identity protection, product brand management, and logo duration monitoring. Particularly, logo detection technology can offer various benefits for companies to help brands measure their logo coverage, track their brand perception, secure their brand value, increase the effectiveness of their marketing campaigns and build brand awareness more effectively. However, compared with the general object detection, logo detection is more challenging due to the existence of both small logo objects and large aspect ratio logo objects. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of two components, namely Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). The former is proposed to fuse semantic features into low-level feature maps to obtain discriminative representation of small logo objects, while the latter is further integrated into DSFP to generate large aspect ratio anchor boxes for detecting large aspect ratio logo objects. Extensive experimental results on four benchmarks demonstrate the effectiveness of the proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the strength of the proposed DSFP-GA when detecting both small logo objects and large aspect logo objects. [ABSTRACT FROM AUTHOR]

Logo detection is one of the crucial branches in computer vision due to various real-world applications, such as automatic logo detection and recognition, intelligent transportation, and trademark infringement detection. Compared with traditional handcrafted-feature-based methods, deep learning-based convolutional neural networks (CNNs) can learn both low-level and high-level image features. Recent decades have witnessed the great feature representation capabilities of deep CNNs and their variants, which have been very good at discovering intricate structures in high-dimensional data and are thereby applicable to many domains including logo detection. However, logo detection remains challenging, as existing detection methods cannot solve well the problems of a multiscale and large aspect ratios. In this paper, we tackle these challenges by developing a novel long-range dependence involutional network (LDI-Net). Specifically, we designed a strategy that combines a new operator and a self-attention mechanism via rethinking the intrinsic principle of convolution called long-range dependence involution (LD involution) to alleviate the detection difficulties caused by large aspect ratios. We also introduce a multilevel representation neural architecture search (MRNAS) to detect multiscale logo objects by constructing a novel multipath topology. In addition, we implemented an adaptive RoI pooling module (ARM) to improve detection efficiency by addressing the problem of logo deformation. Comprehensive experiments on four benchmark logo datasets demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Fruit object detection is crucial for automatic harvesting systems, serving applications such as orchard yield measurement and fruit harvesting. In order to achieve fast recognition and localization of green apples and meet the real-time working requirements of the vision system of harvesting robots, a fast optimized Foveabox detection model (Fast-FDM) is proposed. Fast-FDM uses an optimized form of anchor-free Foveabox to accurately and efficiently detect green apples in harvesting environments. Specifically, the EfficientNetV2-S with fast training and small size is used as the backbone network, a weighted bi-directional feature pyramid network (BiFPN) is employed as the feature extraction network to fuse multi-scale features easily and fast, and then the fused features are fed to the fovea head prediction network for the classification and bounding box prediction. Furthermore, an adaptive training sample selection (ATSS) method is adopted to directly select positive and negative samples, allowing green fruits of different scales to obtain higher recall and achieve more accurate green apple detection. Experimental results show that the proposed Fast-FDM realizes a mean average precision (mAP) of 62.3% for green apple detection using fewer parameters and floating point of operations (FLOPs), achieving better trade-offs between accuracy and detection efficiency. [ABSTRACT FROM AUTHOR]

Logo detection involves the tasks of locating and classifying logo objects in images and videos, and has been widely applied in the real world. However, most existing approaches rely on general object detection strategies that do not fully utilize the unique characteristics of logos. This can lead to sub-optimal performance in complex environments, especially when logos are small or have varying sizes and shapes. We observe that logos belonging to the same category often share similar context information, such as background dependency of the logo. This motivates us to incorporate contextual information to improve logo detection. Our proposed method, Context-based Modeling Enhancement Network (CME-Net), aims to enhance the distinctive region feature of logos using contextual information. We achieve this by modeling both the logo and its background region to extract their contextual information. This contextual information serves as a guide for enhancing the saliency of the distinctive regions within the logo image. To further improve the accuracy of detection, we have implemented a scale feature balance strategy. This strategy solves the problem of losing scale information caused by enhancement, ensuring that all scales are appropriately considered. Additionally, noise generated during the enhancement process is also effectively suppressed. By effectively leveraging contextual information, our method successfully tackles the challenge of accurately locating logo objects. Our extensive experiments on four public benchmark datasets demonstrate that CME-Net improves the accuracy of logo detection in complex environments. • A Context-based Modeling Enhancement Network (CME-Net) is proposed to address the problem of difficult logo detection in complex environments. • A targeted feature enhancement process guided by contextual information is implemented. • CME-Net achieved great performance on four logo detection datasets. [ABSTRACT FROM AUTHOR]