Search Results (3)

With the rapid growth in demand for security surveillance, assisted driving, and remote sensing, object detection networks with robust environmental perception and high detection accuracy have become a research focus. However, single-modality image detection technologies face limitations in environmental adaptability, often affected by factors such as lighting conditions, fog, rain, and obstacles like vegetation, leading to information loss and reduced detection accuracy. We propose an object detection network that integrates features from visible light and infrared images—IV-YOLO—to address these challenges. This network is based on YOLOv8 (You Only Look Once v8) and employs a dual-branch fusion structure that leverages the complementary features of infrared and visible light images for target detection. We designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to effectively integrate multimodal features, reducing errors from feature redundancy and extracting fine-grained features for small object detection. Additionally, we developed a Shuffle-SPP structure that combines channel and spatial attention to enhance the focus on deep features and extract richer information through upsampling. Regarding model optimization, we designed a loss function tailored for multi-scale object detection, accelerating the convergence speed of the network during training. Compared with the current state-of-the-art Dual-YOLO model, IV-YOLO achieves mAP improvements of 2.8%, 1.1%, and 2.2% on the Drone Vehicle, FLIR, and KAIST datasets, respectively. On the Drone Vehicle and FLIR datasets, IV-YOLO has a parameter count of 4.31 M and achieves a frame rate of 203.2 fps, significantly outperforming YOLOv8n (5.92 M parameters, 188.6 fps on the Drone Vehicle dataset) and YOLO-FIR (7.1 M parameters, 83.3 fps on the FLIR dataset), which had previously achieved the best performance on these datasets. This demonstrates that IV-YOLO achieves higher real-time detection performance while maintaining lower parameter complexity, making it highly promising for applications in autonomous driving, public safety, and beyond. Full article

The automatic detection of dermoscopic features is a task that provides the specialists with an image with indications about the different patterns present in it. This information can help them fully understand the image and improve their decisions. However, the automatic analysis of dermoscopic features can be a difficult task because of their small size. Some work was performed in this area, but the results can be improved. The objective of this work is to improve the precision of the automatic detection of dermoscopic features. To achieve this goal, an algorithm named yolo-dermoscopic-features is proposed. The algorithm consists of four points: (i) generate annotations in the JSON format for supervised learning of the model; (ii) propose a model based on the latest version of Yolo; (iii) pre-train the model for the segmentation of skin lesions; (iv) train five models for the five dermoscopic features. The experiments are performed on the ISIC 2018 task2 dataset. After training, the model is evaluated and compared to the performance of two methods. The proposed method allows us to reach average performances of 0.9758, 0.954, 0.9724, 0.938, and 0.9692, respectively, for the Dice similarity coefficient, Jaccard similarity coefficient, precision, recall, and average precision. Furthermore, comparing to other methods, the proposed method reaches a better Jaccard similarity coefficient of 0.954 and, thus, presents the best similarity with the annotations made by specialists. This method can also be used to automatically annotate images and, therefore, can be a solution to the lack of features annotation in the dataset. Full article

Traditional nursery seedling detection often uses manual sampling counting and height measurement with rulers. This is not only inefficient and inaccurate, but it requires many human resources for nurseries that need to monitor the growth of saplings, making it difficult to meet the fast and efficient management requirements of modern forestry. To solve this problem, this paper proposes a real-time seedling detection framework based on an improved YoloV4 network and binocular camera, which can provide real-time measurements of the height and number of saplings in a nursery quickly and efficiently. The methodology is as follows: (i) creating a training dataset using a binocular camera field photography and data augmentation; (ii) replacing the backbone network of YoloV4 with Ghostnet and replacing the normal convolutional blocks of PANet in YoloV4 with depth-separable convolutional blocks, which will allow the Ghostnet–YoloV4 improved network to maintain efficient feature extraction while massively reducing the number of operations for real-time counting; (iii) integrating binocular vision technology into neural network detection to perform the real-time height measurement of saplings; and (iv) making corresponding parameter and equipment adjustments based on the specific morphology of the various saplings, and adding comparative experiments to enhance generalisability. The results of the field testing of nursery saplings show that the method is effective in overcoming noise in a large field environment, meeting the load-carrying capacity of embedded mobile devices with low-configuration management systems in real time and achieving over 92% accuracy in both counts and measurements. The results of these studies can provide technical support for the precise cultivation of nursery saplings. Full article