A scalable multi-modal learning fruit detection algorithm for dynamic environments.

Under the combined action of the expansion of the chemical industry zone and urban boundaries, urban areas exposed to danger are also increasing. As containers for chemical storage, storage tanks are potential sources of hazards. Conducting target detection for hazard risk analysis is essential. YOLOv5, a single-stage algorithm with good performance, accurately identifies most storage tanks with clear contours and evident positions; however, the identification effect is not good for storage tanks with small targets or unclear boundaries. This study proposes an optimized model based on the YOLOv5 model. First, the Coordinate Attention (CA) mechanism is introduced to make the model focus on the effective information of the target. Second, to improve the detection effect of small targets, the model adds a small target detection head and performs multiscale target detection. Finally, the EIOU loss function is employed instead of the CIOU loss function in the original model to improve the algorithm’s accuracy and speed. Experimental results show that the optimized model significantly improves the detection effect on small targets compared with the original YOLOv5 model. The number of small targets detected by the optimized model is significantly increased compared with the original model, and the size of the smallest targets detected by the optimized model is reduced by about twice compared with the original model. The model’s accuracy, recall rate, and mean average precision (mAP@0.5) are improved, which can be better applied to the detection of storage tanks.

Small and dim target detection is a critical challenge in hyperspectral remote sensing, particularly in complex, large-scale scenes where spectral variability across diverse land cover types complicates the detection process. In this paper, we propose a novel target reasoning algorithm named Adaptive Global Dense Nested Reasoning Network (AGDNR). This algorithm integrates spatial, spectral, and domain knowledge to enhance the detection accuracy of small and dim targets in large-scale environments and simultaneously enables reasoning about target categories. The proposed method involves three key innovations. Firstly, we develop a high-dimensional, multi-layer nested U-Net that facilitates cross-layer feature transfer, preserving high-level features of small and dim targets throughout the network. Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. Thirdly, we construct a geographic knowledge graph that incorporates both target and environmental information, enabling global target reasoning and more effective detection of small targets across large-scale scenes. Experimental results on three challenging datasets show that our method outperforms state-of-the-art approaches in detection accuracy and achieves successful classification of different small targets. Consequently, the proposed method offers a robust solution for the precise detection of hyperspectral small targets in large-scale scenarios.

Accurate detection of fresh jujube fruits plays a vital role in precision agriculture, enabling reliable yield estimation and supporting automation tasks such as robotic harvesting. To address the challenges of detecting such small targets (≤32 × 32 pixels) in complex orchard environments, this study proposes JFST-DETR, an efficient and robust detection model based on the Real-Time DEtection TRansformer (RT-DETR). First, to address the insufficient feature representation for small jujube fruit targets, a novel module called the Global Awareness Adaptive Module (GAAM) is designed. Building on GAAM and the innovative Spatial Coding Module (SCM), a new Spatial Enhancement Pyramid Network (SEPN) is proposed. Through the spatial-depth transformation domain and global awareness adaptive processing units, SEPN captures fine-grained features of small targets, enhancing the detection accuracy for small objects. Second, a Dynamic Sampling (DySample) operator is adopted, which optimizes feature space details via dynamic offset calculation and lightweight design, improving detection accuracy while reducing computational costs. Finally, to solve the problem of complex background interference caused by foliage occlusion and illumination variations, Pinwheel-Shaped Convolution (PSConv) is introduced. By using asymmetric padding and multi-directional convolution, PSConv enhances the robustness of feature extraction, ensuring reliable recognition in complex agricultural environments. Experimental results show that JFST-DETR achieves precision, recall, F1, mAP@50, and mAP@50:95 of 93%, 86.8%, 89.8%, 94.3%, and 75.2%. Compared to the baseline model, these metrics improve by 0.8%, 3.7%, 2.4%, 2.6%, and 3.1%, respectively. Cross-dataset evaluations further confirm its strong generalizability, demonstrating potential as a practical solution for small-target detection in intelligent horticulture.

The precise detection of small targets during the Czochralski process for monocrystalline silicon crystal growth is critical for ensuring high-quality production. However, conventional object detection models often face challenges such as inaccurate localization and high false-positive rates, particularly when detecting small protrusions in molten silicon images. To address these challenges, this study introduces an improved YOLOv8-based algorithm, YOLOv8-A, integrating nonlinear mathematical optimization techniques and advanced feature fusion strategies tailored for industrial applications. The proposed model incorporates a Bidirectional Feature Pyramid Network (BiFPN) to enhance multi-scale feature aggregation and a lightweight dynamic upsampling operator (DySample) based on nonlinear interpolation methods to refine feature quality. The nonlinear mathematical formulations incorporated in these components improve the model's ability to capture complex relationships within the data, reducing computational complexity while enhancing detection precision. Experimental validation demonstrates that YOLOv8-A achieves superior performance with a 98.2% mean average precision (mAP) and a 5.8% improvement in small target detection accuracy compared to traditional models. The results underscore the potential of YOLOv8-A as an efficient and robust solution for real-time quality control in silicon crystal growth processes, offering a novel approach to small target detection through mathematical modeling and nonlinear optimization.

In the realm of unmanned aerial vehicles, we proposed an enhanced small target detection algorithm, MGAC-YOLO, to address the challenges of missed detections and low accuracy associated with small target identification. Initially, we designed the MConv (Multi-layer Convolution) module to replace the conventional Conv module within the backbone network, thereby augmenting the dimensionality of information capture and enhancing the detection performance for small targets. Subsequently, we harnessed the advantages of both attention mechanisms-GAM (Global Attention Mechanism) and CloAttention (Contextualized Local and Global Attention)-to create a GACAttention module that extracts small target features from both global and local perspectives, thereby enriching the network's focus on small target feature information and further enhancing its feature processing capabilities. Finally, we incorporated an additional small target detection layer to capture feature information at a shallower level, thereby reducing the likelihood of missed detections and bolstering the detection capabilities for small targets. Experimental results on the VisDrone2019 dataset demonstrate that the Precision, mAP50, and mAP50-95 of the MGAC-YOLO algorithm have improved by 5.3%, 6.3%, and 4.4%, respectively, in comparison to the baseline model YOLOv8s. Furthermore, when compared to other leading algorithms, the MGAC-YOLO algorithm has exhibited notable superiority.

Research on Infrared Dim and Small Target Detection Algorithm Based on Low-Rank Tensor Recovery

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

Dim and small infrared target fast detection guided by visual saliency

Small target detection is a challenging task in very-high-resolution (VHR) optical remote sensing imagery, because small targets occupy a minuscule number of pixels and are easily disturbed by backgrounds or occluded by others. Although current convolutional neural network (CNN)-based approaches perform well when detecting normal objects, they are barely suitable for detecting small ones. Two practical problems stand in their way. First, current CNN-based approaches are not specifically designed for the minuscule size of small targets (~15 or ~10 pixels in extent). Second, no well-established data sets include labeled small targets and establishing one from scratch is labor-intensive and time-consuming. To address these two issues, we propose an approach that combines Sig-NMS-based Faster R-CNN with transfer learning. Sig-NMS replaces traditional non-maximum suppression (NMS) in the stage of region proposal network and decreases the possibility of missing small targets. Transfer learning can effectively label remote sensing images by automatically annotating both object classes and object locations. We conduct an experiment on three data sets of VHR optical remote sensing images, RSOD, LEVIR, and NWPU VHR-10, to validate our approach. The results demonstrate that the proposed approach can effectively detect small targets in the VHR optical remote sensing images of about $10\times 10$ pixels and automatically label small targets as well. In addition, our method presents better mean average precisions than other state-of-the-art methods: 1.5% higher when performing on the RSOD data set, 17.8% higher on the LEVIR data set, and 3.8% higher on NWPU VHR-10.

This study focuses on advancing the field of remote sensing image target detection, addressing challenges such as small target detection, complex background handling, and dense target distribution. We propose solutions based on enhancing the YOLOv7 algorithm. Firstly, we improve the multi-scale feature enhancement (MFE) method of YOLOv7, enhancing its adaptability and precision in detecting small targets and complex backgrounds. Secondly, we design a modified YOLOv7 global information DP-MLP module to effectively capture and integrate global information, thereby improving target detection accuracy and robustness, especially in handling large-scale variations and complex scenes. Lastly, we explore a semi-supervised learning model (SSLM) target detection algorithm incorporating unlabeled data, leveraging information from unlabeled data to enhance the model’s generalization ability and performance. Experimental results demonstrate that despite the outstanding performance of YOLOv7, the mean average precision (MAP) can still be improved by 1.9%. Specifically, under testing on the TGRS-HRRSD-Dataset, the MFE and DP-MLP models achieve MAP values of 93.4% and 93.1%, respectively. Across the NWPU VHR-10 dataset, the three models achieve MAP values of 93.1%, 92.1%, and 92.2%, respectively. Significant improvements are observed across various metrics compared to the original model. This study enhances the adaptability, accuracy, and generalization of remote sensing image object detection.

To detect small infrared targets under the condition of dense clutters, we propose a single-frame target detection algorithm based on a small bounding-box filter, which is characterized by good adaptability to the position and size of a small target. During the small target detection process, the proposed algorithm first searches for the local maximum gray pixel and then, a set of concentric bounding boxes whose center is the pixel found in the first step is constructed, and the detection thresholds of a neighboring region of this pixel are calculated based on the bounding boxes. Finally, the minimum threshold is used to detect small target pixels in the neighboring region. A fast version of the proposed algorithm is a minimum bounding-box filter, which can be implemented by dividing an image into blocks and using the mid-range and range to assess the concentration trend and dispersion of the background. Simulation and analysis results show that the proposed algorithm can achieve high detection probability and low false alarm rate when detecting small targets in the complex background; while its fast version has high computational efficiency. The proposed algorithm can be used in infrared searching and tracking systems.

In the field of infrared remote sensing, the problem of IR small target detection is still an important component part. Concerning infrared dim small target (IRDST) detection, firstly the infrared image is processed with DWT method to get the wavelet coefficients image, but the distribution characteristics, such as scales, frequencies, orientations of wavelet coefficients in different sub-bands are various, so wavelet image denoised by a single threshold criterion can not give a satisfying estimation. Based on this motivation, a spatially adaptive multi-model de-noising strategy (SAMMDS) based IRDST detection method is proposed in this paper, which can adjust thresholding strategy according to the distribution of noise in different scales and directions. Spatially adaptive BayesShrink (SABS) thresholding, traditional BayesShrink (BS) thresholding and generalized cross validation (GCV) thresholding are all adopted here to process each sub-band separately. After reconstructing the denoised wavelet image, a simple global thresholding is used to separate the background and target finally. Experimental results demonstrate that the proposed algorithm performs better than other typical wavelet methods for small target detection with various complex backgrounds.

The infrared image in the sea background usually contains random noise, sea waves, fish scale light and other clutter. Sea waves and fish scale light interference are similar to small infrared targets, so it is difficult to eliminate such interference and detect small infrared targets quickly and accurately. In this paper, a small target detection method based on non-local similarity is proposed. The interference is eliminated by using the class feature similarity of interference and the overall feature uniqueness of the small target. Firstly, this paper performs filtering to remove random noise, and the suspected small targets can be extracted by executing Top Hat filtering. Secondly, a multi-feature vector is constructed for all suspected targets. Finally, this paper analyzes the characteristics of different interference and executes clustering step by step. The wave edge interference and fish scale light interference can be eliminated in turn. Feature threshold is set based on the interference that has been identified to obtain the true small infrared target according to the uniqueness of the characteristics of the true target. The experimental results show that compared with other small target detection algorithms, the proposed algorithm can detect small infrared targets in complex sea background effectively.

The problems of low detection accuracy and high leakage rate of small targets on the water’s surface when observed from the viewpoint of an uncrewed ship are proposed to be resolved by an improved YOLOv7 detection algorithm. Firstly, the parameter-free attention mechanisms SimAM and PConv are introduced into the network to enhance the model’s feature extraction for small targets and reduce the number of model parameters, and SimAM and PConv in the backbone are combined with ELAN1 to form a new efficient layer aggregation network module ELAN1-SAPC. Then, the Head part of the model adopts the newly proposed MP-CXOS structure, which can enhance the model’s focus on small targets and effectively reduce the loss of small target feature information. Finally, the loss function is optimized by incorporating the new NWD metric approach to improve the model’s ability to detect small targets and speed up network convergence. The data set is also processed using data augmentation to improve the model’s generalization. The experiments show that compared with the basic model, the mean Average Precision of the improved YOLOv7 model increases by 5.3 per cent, the F1 score increases by 0.06, and the number of parameters decreases by 20.0 per cent. Therefore, the improved model has significantly improved detection performance and is more lightweight.

Object detection of small and occluded targets in complex scenarios is a vital yet challenging task in computer vision, with applications in intelligent systems (e.g., kitchen safety supervision). To address limitations of existing models, this study proposes an improved YOLOv10 algorithm with three key innovations. We first introduce a Mosaic-9 data augmentation strategy to enhance small target density in training samples. The traditional PANet in YOLOv10 is replaced by the Bidirectional Feature Pyramid Network (BiFPN), which uses cross-scale bidirectional connections and learnable weights to optimize multi-scale feature fusion. A Squeeze-and-Excitation (SE) channel attention module is integrated into the CSPDarknet backbone to emphasize key feature channels and mitigate background interference for occluded objects. Experiments on a self-constructed dataset (6508 images with multi-scale and occluded targets) show the improved YOLOv10 achieves 69.5% mAP@0.5, a 7.7 percentage point increase over YOLOv10n, while maintaining 12.1 ms inference speed. Ablation studies verify Mosaic-9 enhances small target perception, BiFPN boosts mAP@0.5 by 5.7%, and SE improves occlusion robustness by 4.8%. This work offers a generalizable multi-module optimization framework for YOLO-series models, applicable to various small and occluded target detection tasks, advancing lightweight object detection algorithms and intelligent vision systems.