Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption. To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8. Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model. Secondly, an attention mechanism called BiFormer is introduced to optimize the backbone network, which improves the model’s attention to critical information. Finally, we design a feature processing module named Focal FasterNet block (FFNB) and propose two new detection scales based on this module, which makes the shallow features and deep features fully integrated. The proposed multiscale feature fusion network substantially increased the detection performance of the model and reduces the missed detection rate of small objects. The experimental results show that our model has fewer parameters compared to the baseline model and has a mean detection accuracy higher than the baseline model by 7.7%. Compared with other mainstream models, the overall performance of our model is much better. The proposed method effectively improves the ability to detect small objects. There is room to optimize the detection effectiveness of our model for small and feature-less objects (such as bicycle-type vehicles), as we will address in subsequent research.

In the field of computer vision, fine-grained image retrieval is an extremely challenging task due to the inherently subtle intra-class object variations. In addition, the high-dimensional real-valued features extracted from large-scale fine-grained image datasets slow the retrieval speed and increase the storage cost. To solve above issues, existing fine-grained image retrieval methods mainly focus on finding more discriminative local regions for generating discriminative and compact hash codes, which achieve limited fine-grained image retrieval performance due to the large quantization errors and the confounding granularities and context of discriminative parts, i.e., the correct recognition of fine-grained objects mainly attribute to the discriminative parts and their context. To learn robust causal features and reduce the quantization errors, we propose a deep progressive asymmetric quantization (DPAQ) method based on causal intervention to learn compact and robust descriptions for fine-grained image retrieval task. Specifically, we introduce a structural causal model to learn robust casual features via causal intervention for fine-grained visual recognition. Subsequently, we design a progressive asymmetric quantization layer in the feature embedding space, which can preserve the semantic information and reduce the quantization errors sufficiently. Finally, we incorporate both the fine-grained image classification and retrieval tasks into an end-to-end deep learning architecture for generating robust and compact descriptions. Experimental results on several fine-grained image retrieval datasets demonstrate that the proposed DPAQ method performs the best for fine-grained image retrieval task and surpasses the state-of-the art fine-grained hashing methods by a large margin.

Low-quality remotely sensed images (RSIs) are not beneficial for the analysis of many activities including agricultural growth, resident migration, forest fire, and etc. Many previous enhancement schemes improve their quality via changing their illumination. However, these approaches often fail in detail and brightness preservation as well as contrast improvement due to that the information from a single image is limited. To address this issue, an enhancement framework, named as global-local enhancement network (GLE-Net), is proposed to correct the intensity via learning extra information from collected training data, including the following three key steps: first, RSIs are decomposed by the discrete wavelet transformation (DWT) method into the low-frequency component and the detail components. Then, the low-frequency component is improved by the global enhancement network while the detail components are enhanced by the local enhancement network in parallel. Finally, the enhanced components are employed to produce high-quality images with the inverse DWT (IDWT) method. The quantitatively and qualitatively comparable experiments on both synthetic and real-world RSIs validate that the proposed GLE-Net method performs well on preserving brightness and fine details, and even outperforms the state-of-the-arts.

The aim of weakly supervised object co-localization is to locate different objects of the same superclass in a dataset. Recent methods achieve impressive co-localization performance by multiple instance learning and self-supervised learning. However, these methods ignore the common part information shared by fine-grained objects and the influence of the complementary parts on the co-localization of the fine-grained objects. To solve these issues, we propose a complementary parts contrastive learning method for fine-grained weakly supervised object co-localization. The proposed method follows such an assumption that fine-grained object parts with the same/different semantic meaning should have similar/dissimilar feature representations in the feature space. The proposed method tackles two critical issues in this task: i ) how to spread the model’s attention and suppress the complex background noise, and ii ) how to leverage the cross-category common parts information to mitigate the context co-occurrence problem. To address i ), we attempt to integrate local and context cues via three types of attention including self-supervised attention, channel, and spatial attention to spread the model’s attention toward automatically identifying and localizing most discriminative parts of objects in the fine-grained images. To solve ii ), we propose a cross-category object complementarity part contrastive learning module to identify the extracted part regions with different semantic information by pulling the same part features closer and pushing different part features away, which can mitigate the confounding bias caused by the co-occurrence surroundings within specific classes. Extensive qualitative and quantitative evaluations demonstrate the effectiveness of the proposed method on four fine-grained co-localization datasets: CUB-200-2011, Stanford Cars, FGVC-Aircraft, and Stanford Dogs. Code and models are available at https://github.com/Zhao-fan/CPCL.

Image retrieval with fine-grained categories is an extremely challenging task due to the high intraclass variance and low interclass variance. Most previous works have focused on localizing discriminative image regions in isolation, but have rarely exploited correlations across the different discriminative regions to alleviate intraclass differences. In addition, the intraclass compactness of embedding features is ensured by extra regularization terms that only exist during the training phase, which appear to generalize less well in the inference phase. Finally, the information granularity of the distance measure should distinguish subtle visual differences and the correlation between the embedding features and the quantized features should be maximized sufficiently. To address the above issues, we propose a logit variated product quantization method based on part interaction and metric learning with knowledge distillation for fine-grained image retrieval. Specifically, we introduce a causal context module into the deep navigator to generate discriminative regions and utilize a channelwise cross-part fusion transformer to model the part correlations while alleviating intraclass differences. Subsequently, we design a logit variation module based on a weighted sum scheme to further reduce the intraclass variance of the embedding features directly and enhance the learning power of the quantization model. Finally, we propose a novel product quantization loss based on metric learning and knowledge distillation to enhance the correlation between the embedding features and the quantized features and allow the quantization features to learn more knowledge from the embedding features. The experimental results on several fine-grained datasets demonstrate that the proposed method is superior to state-of-the-art fine-grained image retrieval methods.