A Comparative Study of Nighttime Object Detection With Datasets From Australia and China

In this paper, we will study the basic flow and the principle of state-of-the-art object detection technique (i.e. Faster R-CNN) and improve it further with the inclusion of two strategies into it. Firstly, we propose a multi-layer features merging strategy by using a concatenation layer. Secondly, we introduce a contextual learning scheme for Faster R-CNN. Previously, Faster R-CNN just uses regional features. Contextual features are added with the regional features for the classification and detection task. Our improvement on Faster R-CNN shows promising results. We call our improved Faster R-CNN network as ID-CNN (Intelligent Detection Using Convolutional Neural Network) as its detection accuracy is better. Therefore, we call it as an intelligent detector. We use a deep VGG-16 model as our base model, as Faster R-CNN did. We evaluated our ID-CNN on Pascal VOC public datasets. Experimental results show that ID-CNN can effectively improve the object detection average precision to some extent. On VOC 2007 and 2012, we achieved a mean average precision (mAP) of 74.7% and 71.9%, respectively. ID-CNN is also end-to-end trainable with the same alternating fine-tuning optimization scheme of Faster R-CNN. Finally, we compared ID-CNN with Faster R-CNN on ImageNet object detection dataset and we achieved mAP of 48.1% compared with 46.2% for Faster R-CNN.

In real‐time, visually impaired persons face challenging issues in identifying and avoiding obstacles in their path, making it difficult to move around freely and confidently. Object detection technology benefits visually impaired persons and contains the procedure of determining several individual objects from the image and recognizing their place with bounding boxes with class labels. Both exploit typical machine learning (ML) approaches and utilize deep learning (DL) models to execute object detection methods. Recently, several researchers have leveraged DL techniques such as convolutional neural networks (CNNs) for solving object detection problems and presented various recent object detection approaches. The study presents a sine cosine optimization with DL–driven object detector (SCODL–ODC) technique for visually impaired persons. The presented SCODL–ODC technique detects and classifies the objects, which can help them navigate their environment more efficiently and avoid obstacles. To accomplish this, the proposed SCODL–ODC technique employs a YOLOv5 object detector at the initial stage to detect the objects accurately, and the SCO model performs its hyperparameter tuning process. The following step is to classify them using the deep stacked autoencoder (DSAE) model when the objects are detected. Finally, the Harris Hawks optimization (HHO) technique is used to adjust the hyperparameters of the DSAE model. The experimentation outcome of the SCODL–ODC method is examined on the object detection dataset. The investigational validation of the SCODL–ODC method portrayed a superior accuracy value of 99.31% over existing models.

Detection of metastasis of mediastinal lymph nodes in lung cancer patients with an artificial intelligence model.

Cancer is one of the most life-threatening and devastating diseases in the world. The generally recognized standard for cancer staging is the TNM staging system. In addition, a new parameter called the Immunoscore has been developed recently to assess the survival rate of patients. The Immunoscore is based on counts of CD3+ and CD8+ lymphocytes in the tumour core and margin. Counting the number of lymphocytes is a tedious process for pathologists. This paper examines the use of deep learning techniques for automatic detection and counting of lymphocytes from immunohistochemistry images of breast, colon, and prostate cancers. We used an object detector called Faster R-CNN with four feature extractors: Resnet-50, VGG-16, Inception-V2, and Resnet-101 for automatic detection and counting of lymphocytes. A total of 11,136 lymphocytes were annotated after performing data augmentation on 1228 images. The test images are separated into three regions of interest (ROI): scattered lymphocytes, groups of lymphocytes, and artefacts. In each ROI, the performance of the object detector was checked by evaluation metrics. On comparing the F1-score for all three ROIs, we found that Resnet-101 provides better performance than the other feature extractors when using Faster R-CNN. The mean error in lymphocyte count for all ROIs appears to be insignificant. The detection time for a single image is less for VGG-16 than for other feature extractors. This study presents a fine-tuned Faster R-CNN object detector for automatic detection and counting of lymphocytes in three different cancer tissues for scoring lymphocytes. Our results suggest that the Faster R-CNN method is efficient and yields good results. Thus, the proposed method can assist pathologists in providing a cancer prognosis.

To identify and locate industrial textile defects accurately, this study proposes a textile detection model based on a convolution neural network (CNN) known as Faster R-CNN. First, a textile defect feature map was extracted by ResNet-101 deep convolution network. Faster R-CNN only extracts features from the last layer of the feature map, which leads to a loss of low-level location information. The proposed method adds the feature pyramid network (FPN) to the network architecture to make an independent prediction for each level in the feature extraction stage. The extracted feature map is input into the regional proposal network, among which the overlapping regional proposals are suppressed. The proposed improved Faster R-CNN model with Region Proposal Network (RPN), Soft Non-Maximum Suppression (NMS), and Region of Interest (ROI) Align can achieve a detection accuracy of 98% and an mean of Average Precision (mAP) of 85%, which is more competitive than the state-of-the-art deep learning-based object detection algorithms.

<title>Abstract</title> Accurate and efficient object detection is essential for intelligent road-scene monitoring systems operating in visually complex and resource-constrained environments. While one-stage detectors achieve high inference speed, they often struggle with precise localization of small or low-contrast objects, whereas two-stage detectors provide higher accuracy at the cost of increased latency. To address this trade-off, this paper proposes a hybrid object-detection architecture that integrates You Only Look Once version 5-Small (YOLOv5s) as a fast proposal generator with Faster Region-Based Convolutional Neural Network (Faster R-CNN) as a region-wise refinement module. The proposed framework replaces the Region Proposal Network of Faster R-CNN with high-confidence YOLOv5s detections and employs confidence-weighted fusion to produce spatially consistent final predictions. The hybrid model was evaluated on complex road-scene data using standard object-detection metrics, including mean Average Precision at IoU 0.50 (mAP@50), precision, recall, and inference speed. Experimental results show that the proposed approach achieves mAP@50 of 0.89, improving upon the YOLOv5s baseline by 4.7 percentage points, while maintaining near–real-time performance at 45 frames per second, which is approximately three times faster than a standalone Faster R-CNN. The hybrid detector also attained a precision of 0.93 and a recall of 0.90, demonstrating improved localization accuracy and reduced false detections, particularly for small and visually ambiguous road-scene objects. Repeated experiments confirmed the robustness of the approach, with consistent accuracy gains and low variance across runs. These results demonstrate that strategically combining one-stage and two-stage detection paradigms can yield a favorable accuracy–efficiency balance, making the proposed hybrid architecture suitable for practical deployment in intelligent road-infrastructure and smart-city applications.

FPN (Feature Pyramid Network) and transformer-based target detectors are commonly employed in target detection tasks. However, these approaches suffer from design flaws that restrict their performance. To overcome these limitations, we proposed TIG-DETR (Texturized Instance Guidance DETR), a novel target detection model. TIG-DETR comprises a backbone network, TE-FPN (Texture-Enhanced FPN), and an enhanced DETR detector. TE-FPN addresses the issue of texture information loss in FPN by utilizing a bottom-up architecture, Lightweight Feature-wise Attention, and Feature-wise Attention. These components effectively compensate for texture information loss, mitigate the confounding effect of cross-scale fusion, and enhance the final output features. Additionally, we introduced the Instance Based Advanced Guidance Module in the DETR-based detector to tackle the weak detection of larger objects caused by the limitations of window interactions in Shifted Window-based Self-Attention. By incorporating TE-FPN instead of FPN in Faster RCNN and employing ResNet-50 as the backbone network, we observed an improvement of 1.9 AP in average accuracy. By introducing the Instance-Based Advanced Guidance Module, the average accuracy of the DETR-based target detector has been improved by 0.4 AP. TIG-DETR achieves an impressive average accuracy of 44.1% with ResNet-50 as the backbone network.

This composition bargains with the Tomato leaf infection discovery and classification utilizing different strategies like Convolutional Neural Network (CNN), Regions with CNN (R-CNN), Fast R-CNN and Faster R-CNN. The main issue in the agricultural sector is leaf diseases, which have an impact on crop yield and financial gain. Early detection of leaf diseases in plants is essential to prevent losses to the agricultural sector. There are several tomato leaf diseases that affect the crop's leaves, including the Mosaic virus, Early Blight, Healthy, Septoria leaf, and Bacterial spot. Using deep learning algorithms and image processing methods, we can identify the diseases in tomato leaves using developing deep learning approaches. The implementation procedures in our proposed work involves data collection, pre-processing, training, feature extraction, testing, and classification utilising the Visual Geometry Group (VGG 16) to identify damaged or healthy leaves. VGG 16 is incorporated to categorise the leaves as healthy or diseased based on the data and Regression's boundary box method is adopted. Therefore, using Faster RCNN, a model is created to identify and categorise diseases from every image of a tomato leaf that is used as an input, providing a forecast with a considerably greater degree of accuracy. We obtain an accuracy of approximately 98% after fitting the collected features into the neural network over 20 iterations.

Timely fault diagnosis and localization of transmission lines is crucial for ensuring the reliable operation of increasingly complex power systems. This study proposes an optimized R-Net algorithm based on a feature pyramid network (FPN) and densely connected convolutional network (D-Net) for transmission line fault diagnosis and localization. The R-Net network is enhanced by reshaping the anchor points using an improved K-means algorithm and incorporating an FPN for multi-scale feature extraction. The backbone network is further optimized using D-Net to strengthen inter-layer connections and improve feature reuse. Experimental results demonstrate that the optimized R-Net achieves an overall average accuracy of 0.64, outperforming the original network by 1.30%. The accuracy improvement is particularly significant for ground wire defects (2.40%). The D-Net-based R-Net, despite having fewer parameters, maintains high accuracy (0.6502). Compared to other object detection algorithms, such as YOLO-v3 and Faster R-CNN, the optimized R-Net exhibits superior performance in terms of mean average precision (15.58% and 2.45% higher, respectively) and parameter efficiency (17M vs. 38M and 81M). Considering both performance and speed, the optimized R-Net achieves a processing rate of 10.5 frames per second. This study provides an efficient and accurate tool for transmission line fault diagnosis and localization, with significant practical implications for power system operation and maintenance.

Feature extraction methods of multi-spectral remote sensing images is of great significance for remote sensing image analysis, but it still faces some challenges. The ability of traditional feature extraction methods based on artificial features or shallow machine learning have some shortcomings and limitations. Recently, a series of proposed R-CNN networks, especially Faster R-CNN, have achieved excellent results in the field of target recognition. However, Faster R-CNN for multi-spectral imagery object detection has several drawbacks: (1) the object spectral information cannot be fully utilized in Faster R-CNN used to process RGB images; (2) the spatial semantic relationship information which could not be mined by Faster R-CNN among remote sensing image features can improve the feature extraction ability of the network; (3) objects occupy relatively few pixels because of the low resolution of multi-spectral images, and Faster R-CNN has poor detection performance for small objects. To address the above problems, we propose an effective and novel object detection method for multi-spectral images with small objects. First, we design a feature extractor by adopting a 3D convolution neural network which can simultaneously extract spectral information and spatial information. Secondly, an object relation module for mining context information is introduced into the network. Finally, in order to solve the problem of small targets, a multi-scale object proposal network for generating regions of objects from several intermediate layers is used. We conducted a set of controlled trials on the satellite imagery feature detection dataset released by Dstl on the Kaggle website and the results showed that our approach was very effective.

In general object detection, scale variation is always a big challenge. At present, feature pyramid networks are employed in numerous methods to alleviate the problems caused by large scale range of objects in object detection, which makes use of multi-level features extracted from the backbone for top-down upsampling and fusion to acquire a set of multi-scale depth image features. However, the feature pyramid network proposed by Ghiasi et al. adopts a simple fusion method, which fails to consider the fusion feature context, and therefore, it is difficult to acquire good features. In addition, the fusion of multi-scale features directly by traditional upsampling is prone to feature misalignment and loss of details. In this paper, an adaptive feature pyramid network is proposed based on the feature pyramid network to alleviate the foregoing potential problems, which includes two major designs, i.e., adaptive feature upsampling and adaptive feature fusion. The adaptive feature upsampling aims to predict a group of sampling points of each pixel through some models, and constitute feature representation of the pixel by feature combination of sampling points, while adaptive feature fusion is to construct pixel-level fusion weights between fusion features through attention mechanism. The experimental results verified the effectiveness of the method proposed in this paper. On the public object detection dataset MS-COCO test-dev, Faster R-CNN model achieved performance improvement of 1.2 AP by virtue of the adaptive feature pyramid network, and FCOS model could achieve performance improvement of 1.0 AP. What's more, the experiments also validated that the adaptive feature pyramid network proposed herein was more accurate for object localization.

This project presents an efficient object detection algorithm in digital images. Traditional object detection methods, particularly those based on deep learning, offer high accuracy but are often resource-intensive, making them unsuitable for deployment on low-power or real-time embedded systems. To address these chal- lenges, the proposed algorithm focuses on object detection using Otsu threshold- ing, followed by a window-based scanning mechanism and Faster R-CNN boundary expansion technique to accurately identify and isolate object boundaries. The al- gorithm is implemented using Python and Open CV, and tested on real-world datasets, such as traffic images containing closely packed vehicles. It offers a tun- able trade-off between detection resolution and processing time, making it adapt- able for various application needs. The system operates independently of object shape or size, and provides a robust solution for environments where computa- tional resources are limited. Experimental results demonstrate the effectiveness of the approach in achieving accurate detection with minimal overhead. This makes the algorithm well-suited for applications such as traffic surveillance, industrial sorting, and smart agriculture, with future work focused on enhancing robustness to lighting variations and extending support for object tracking in video streams.

Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extracting a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. This paper is motivated by the need to overcome the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought about by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP improvement on bounding box detection and 1.7% AP improvement on instance segmentation. The code is publicly available on GitHub repository at https://github.com/IMPLabUniPr/mmdetection/tree/groie_dev

The application of base on deep learning remote sensing image landslide detection has become a trend. In view of the, large difference in the scale of landslide objects and the very important characteristics of the shallow layer of the landslide object, we modified the original Faster RCNN. First compare the average accuracy values of different network models, and select the network ResNet50 with deeper layers, and then integrated into Feature Pyramid Network (FPN) on the basis of ResNet50, Fusion of FPN structure with RPN and Fast RCNN. Finally, it is verified on the public landslide dataset in BiJie City, Guizhou Province, China, and the average accuracy of the obtained model is 87.10%, which is 16.9% higher than the 70.20% of VGG16. Contrast experiments with different anchor ratios and different hyperparameters in the FPN part are carried out, and the generated results are analyzed and compared. The experimental results show that the proposed method has good robustness and high accuracy for the detection of landslide objects, in remote sensing images, which can provide a technical reference for further research on landslide detection in remote sensing images.

Visual object detection is a critical task for a variety of industrial applications, such as robot navigation, quality control and product assembling. Modern industrial environments require AI-based object detection methods that can achieve high accuracy, robustness and generalization. To this end, we propose a novel object detection approach that can process and fuse information from RGB-D images for the accurate detection of industrial objects. The proposed approach utilizes a novel Variational Faster R-CNN algorithm that aims to improve the robustness and generalization ability of the original Faster R-CNN algorithm by employing a VAE encoder-decoder network and a very powerful attention layer. Experimental results on two object detection datasets, namely the well-known RGB-D Washington dataset and the QCONPASS dataset of industrial objects that is first presented in this paper, verify the significant performance improvement achieved when the proposed approach is employed.