Light field salient object detection (SOD) is an essential research topic in computer vision, but robust saliency detection in complex scenes is still very challenging. We propose a new method for accurate and robust light field SOD via convolutional neural networks containing feature enhancement modules. First, the light field dataset is extended by geometric transformations such as stretching, cropping, flipping, and rotating. Next, two feature enhancement modules are designed to extract features from RGB images and depth maps, respectively. The obtained feature maps are fed into a two-stream network to train the light field SOD. We propose a mutual attention approach in this process, extracting and fusing features from RGB images and depth maps. Therefore, our network can generate an accurate saliency map from the input light field images after training. The obtained saliency map can provide reliable a priori information for tasks such as semantic segmentation, target recognition, and visual tracking. Experimental results show that the proposed method achieves excellent detection performance in public benchmark datasets and outperforms the state-of-the-art methods. We also verify the generalization and stability of the method in real-world experiments.

An event camera (EC) is a bioinspired vision sensor with the advantages of a high temporal resolution, high dynamic range, and low latency. Due to the inherent sparsity of space target imaging data, EC becomes an ideal imaging sensor for space target detection. In this work, we conduct detection of small space targets using a CeleX-V camera with a megapixel resolution. We propose a target detection method based on field segmentation, utilizing the event output characteristics of an EC. This method enables real-time monitoring of the spatial positions of space targets within the camera’s field of view. The effectiveness of this approach is validated through experiments involving real-world observations of space targets. Using the proposed method, real-time observation of space targets with a megapixel resolution EC becomes feasible, demonstrating substantial practical potential in the field of space target detection.

Yolo series models are extensive within the domain of object detection. Aiming at the challenge of small object detection, we analyze the limitations of existing detection models and propose a Vis-YOLO object detection algorithm based on YOLOv8s. First, the down-sampling times are reduced to retain more features, and the detection head is replaced to adapt to the small object. Then, deformable convolutional networks are used to improve the C2f module, improving its feature extraction ability. Finally, the separation and enhancement attention module is introduced to the model to give more weight to the useful information. Experiments show that the improved Vis-YOLO model outperforms the YOLOv8s model on the visdrone-2019 dataset. The precision improved by 5.4%, the recall by 6.3%, and the mAP50 by 6.8%. Moreover, Vis-YOLO models are smaller and suitable for mobile deployment. This research provides a new method and idea for small object detection, which has excellent potential application value.

X-ray imaging is essential for security inspection; nevertheless, the penetrability of X-rays can cause objects within a package to overlap in X-ray images, leading to reduced accuracy in manual inspection and increased difficulty in auxiliary inspection techniques. Existing methods mainly focus on object detection to enhance the detection ability of models for overlapping regions by augmenting image features, including color, texture, and semantic information. However, these approaches do not address the underlying issue of overlap. We propose a novel method for separating overlapping objects in X-ray images from the perspective of image inpainting. Specifically, the separation method involves using a vision transformer (ViT) to construct a generative adversarial network (GAN) model that requires a hand-created trimap as input. In addition, we present an end-to-end approach that integrates Mask Region-based Convolutional Neural Network with the separation network to achieve fully automated separation of overlapping objects. Given the lack of datasets appropriate for training separation networks, we created MaskXray, a collection of X-ray images that includes overlapping images, trimap, and individual object images. Our proposed generative separation network was tested in experiments and demonstrated its ability to accurately separate overlapping objects in X-ray images. These results demonstrate the efficacy of our approach and make significant contributions to the field of X-ray image analysis.