Small Object Detection Research Articles

CBS-YOLOv5: fault detection algorithm of electrolyzer plate with low-resolution infrared images based on improved YOLOv5

Abstract In the process of copper electrorefining, accurate detection of electrode plate faults is extremely challenging due to the low resolution of captured infrared images, significant noise interference, and dense electrode plate arrangements. To address these challenges, this paper proposes an improved YOLOv5-based electrode plate fault detection algorithm called CBS-YOLOv5. This algorithm introduces several innovations over the original YOLOv5, including: the incorporation of coordinate attention to enhance the ability of the feature extraction network to separate target information from noise; the construction of a small object detection module to improve the detection of dense small objects by increasing the resolution of the feature map; the replacement of the traditional path aggregation network with a Bi-directional Feature Pyramid Network (BiFPN) for more flexible multi-scale feature fusion; and the integration of the swin transformer to optimize the cross-stage partial bottleneck structure, significantly enhancing the model’s ability to detect densely packed small objects. Experimental results show that the proposed CBS-YOLOv5 model achieves an accuracy of 88.1%, which is an improvement of 5.7% over the base model. Furthermore, this algorithm demonstrates exceptional detection capabilities for dense small objects in low-resolution infrared images while maintaining real-time detection speed, making it suitable for various complex industrial scenarios, including fault detection in non-ferrous metal electrolysis processes. CBS-YOLOv5 not only improves detection accuracy and robustness but also has broad application prospects, offering a new solution for intelligent manufacturing and industrial inspection.

Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts

Sugarcane is a crop that propagates through seed sprouts on nodes. Accurate identification of sugarcane seed sprouts is crucial for sugarcane planting and the development of intelligent sprout-cutting equipment. This paper proposes a sugarcane seed sprout recognition method based on the YOLOv8s model by adding the simple attention mechanism (SimAM) module to the neck network of the YOLOv8s model and adding the spatial-depth convolution (SPD-Conv) to the tail convolution part. Meanwhile, the E-IoU loss function is chosen to increase the model’s regression speed. Additionally, a small-object detection layer, P2, is incorporated into the feature pyramid network (FPN), and the large-object detection layer, P5, is eliminated to further improve the model’s recognition accuracy and speed. Then, the improvement of each part is tested and analyzed, and the effectiveness of the improved modules is verified. Finally, the Sugarcane-YOLO model is obtained. On the sugarcane seed and sprout dataset, the Sugarcane-YOLO model performed better and was more balanced in accuracy and detection speed than other mainstream models, and it was the most suitable model for seed and sprout recognition by automatic sugarcane-cutting equipment. Experimental results showed that the Sugarcane-YOLO achieved a mAP50 value of 99.05%, a mAP72 value of 81.3%, a mAP50-95 value of 71.61%, a precision of 97.42%, and a recall rate of 98.63%.

Surface defect inspection of industrial products with object detection deep networks: a systematic review

One of the focal points in industrial product defect detection lies in the utilization of deep learning-based object detection algorithms. With the continuous introduction of these algorithms and their refined models, notable achievements have been attained. However, challenges persist in industrial settings, such as substantial variations in defect scales, the delicate balance between accuracy and speed, and the detection of small objects. Various methods have been proposed to address these challenges and propel the advancement of defect detection. To comprehensively review the latest developments in deep learning-based industrial product defect detection algorithms and foster further progress, this paper encompasses typical datasets and evaluation metrics used in industrial product defect detection, traces the development history of supervised one-stage and two-stage object detection algorithm-based and unsupervised algorithm-based industrial defect detection methods, discusses major challenges, and outlines future directions. It highlights the potential for further improving the accuracy, speed, and reliability of defect detection systems in industrial applications.

Improving rail transit security with enhanced YOLOv5 obstacle detection

Abstract As the economy advances and rail transit technology progresses, rail transit plays an important role in alleviating urban traffic pressure and promoting regional economic development. However, railway safety faces potential hazards such as foreign object intrusion in the trackside environment, seriously threatening the safety of trains and passengers. Therefore, to achieve high-performance recognition of obstacle target images in rail transit, a small sample object detection algorithm YOLOv5-RTO, which using EVC (Explicit Visual Center) attention mechanism and lightweight up-sampling operator CARAFE (Content-Aware ReAssembly of FEatures) to improve YOLOv5, has been proposed. Based on the self-collected image data of rail transit obstacles, a dense and time-varying dataset was created within the rail transit system, and data augmentation methods were used to reduce the impact of small sample datasets on the detection performance of subsequent algorithm. Using this dataset, target detection tests were conducted on rail transit obstacle images to analyze the actual performance of the training model. The experiment results indicate that the enhanced YOLOv5-RTO algorithm integrates the advantages of EVC and CARAFE while preserving the lightweight and accurate characteristics of YOLOv5. The improved algorithm scheme has an average inference time of 1.3 ms per frame in the field of rail transit images, achieving an overall mAP of 96.5%, an increase of 1.8% compared to the original algorithm. It has a certain engineering application value and provides reference for further optimizing the safety system of urban rail transit.

Enhanced Abandoned Object Detection through Adaptive Dual-Background Modeling and SAO-YOLO Integration

Abandoned object detection is a critical task in the field of public safety. However, existing methods perform poorly when detecting small and occluded objects, leading to high false detection and missed detection rates. To address this issue, this paper proposes an abandoned object detection method that integrates an adaptive dual-background model with SAO-YOLO (Small Abandoned Object YOLO). The goal is to reduce false and missed detection rates for small and occluded objects, thereby improving overall detection accuracy. First, the paper introduces an adaptive dual-background model that adjusts according to scene changes, reducing noise interference in the background model. When combined with an improved PFSM (Pixel-based Finite State Machine) model, this enhances detection accuracy and robustness. Next, a network model called SAO-YOLO is designed. Key improvements within this model include the SAO-FPN (Small Abandoned Object FPN) feature extraction network, which fully extracts features of small objects, and a lightweight decoupled head, SODHead (Small Object Detection Head), which precisely extracts local features and enhances detection accuracy through multi-scale feature fusion. Finally, experimental results show that SAO-YOLO increases mAP@0.5 and mAP@0.5:0.95 by 9.0% and 5.1%, respectively, over the baseline model. It outperforms other advanced detection models. Ultimately, after a series of experiments on the ABODA, PETS2006, and AVSS2007 datasets, the proposed method achieved an average detection precious of 91.1%, surpassing other advanced methods. It significantly outperforms other advanced detection methods. This approach notably reduces false and missed detections, especially for small and occluded objects.

Self-supervised monocular depth estimation based on improved dense network and wavelet decomposition

Traditional self-supervised monocular depth estimation models do not adequately extract and fuse shallow features, which can easily lead to problems such as missed detection of small objects and blurred object edges. To address the above problems, this paper proposes a self-supervised monocular depth estimation model based on improved dense network and wavelet decomposition. The model follows the structure of U-net, in which the encoder adopts an improved dense connection method to improve the feature extraction and fusion capabilities of the encoder; at the same time, a detail enhancement module is added to the jump connection to further refine and integrate the multi-scale features output by the encoder; finally, the wavelet decomposition method is introduced in the decoder, forcing the decoder to pay more attention to high-frequency information and achieve refined processing of image edges. Experimental results show that the depth estimation model proposed in this paper has a stronger ability to capture small object features, and the edges of the generated depth map are clearer and more accurate.

A Novel YOLOv5_ES based on lightweight small object detection head for PCB surface defect detection

In the manufacturing process of printed circuit boards (PCBs), surface defects have a significant negative impact on product quality. Considering that traditional object detection algorithms have low accuracy in handling PCB images with complex backgrounds, various types, and small-sized defects, this paper proposes a PCB defect detection algorithm based on a novel YOLOv5 multi-scale attention mechanism(EMA) spatial pyramid dilated Convolution (SPD-Conv) (YOLOv5_ES) network improved YOLOv5s framework. Firstly, the detection head is optimized by removing medium and large detection layers, fully leveraging the capability of the small detection head to identify minor target defects. This approach not only improves model accuracy but also achieves lightweighting. Secondly, in order to further reduce the number of parameters and computational costs, the SPD-Conv is introduced to improve the feature extraction capability by reducing information loss. Thirdly, a EMA module is introduced to fuse context information of different scales, enhancing the model’s generalization ability. Compared to the YOLOv5s model, there is a 3.1% improvement in mean average precision (mAP0.5), a 55.8% reduction in model parameters, and a 4.8% reduction in giga floating-point operations per second (GFLOPs). These results demonstrate a significant improvement in both accuracy and model parameter efficiency.

A small object detection architecture with concatenated detection heads and multi-head mixed self-attention mechanism

A small object detection architecture with concatenated detection heads and multi-head mixed self-attention mechanism

A Novel Pre-Processing Approach and Benchmarking Analysis for Faster, Robust, and Improved Small Object Detection Methods

A Novel Pre-Processing Approach and Benchmarking Analysis for Faster, Robust, and Improved Small Object Detection Methods

SOD‐YOLO: Small Object Detection Network for UAV Aerial Images

With the rapid development of the UAV industry, object detection using UAV has become a research hotspot. However, most current object detection models based on deep learning have large parameter counts and are difficult to deploy on embedded devices with limited memory and computational power. To address this problem, a Small Object Detection network for UAV aerial images SOD‐YOLO based on YOLOv8 is proposed, which can meet the application requirements of resource‐constrained devices while ensuring the detection accuracy. First, cross‐domain fusion attention (CDFA) mechanism is proposed to build the C2f‐Attention module in this paper, which is embedded in the backbone network in order to improve the extraction capability of key object features. Meanwhile, the AIFI_LSPE feature fusion module with improved RT‐DETR and the IoU‐aware query selection mechanism are added to the path aggregation network to improve the accuracy of multi‐scale object detection. In addition, in order to balance the sample size ratio and improve the robustness of the network model, we make a new UAV image dataset named VisDrone2019 Extended Edition (VDEE) using images from the VisDrone2019 and UAVDT public datasets. Finally, Shape‐IoU is used as a loss function to reduce the difference between the object GT frame and the detection frame. Experiments show that SOD‐YOLO has a mAP@0.5 of 42.8% in the VDEE dataset, which is increased by 5.1% over YOLOv8. In the VisDrone2019 dataset mAP@0.5 is 39.2%, an improvement of 5.8% over YOLOv8. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Excellent tomato detector based on pruning and distillation to balance accuracy and lightweight

Rapid and accurate detection of tomatoes in natural plantation environments is the linchpin for smart harvesting. However, the complexity of existing models necessitates high hardware computing power, constraining the deployment of tomato recognition models in picking robots. In this study, the overall scheme framework of the vision algorithm is divided into three stages. In the first stage, based on YOLOv8s, a small object detection layer was added along with hyperparameter optimization, resulting in the proposed YOLOv8-TomatoTeacher (YOLOv8-TT) to improve the accuracy and precise positioning of tomato detection, laying the foundation for subsequent operations. In the second stage, the pruning algorithm is applied to the trained YOLOv8-TT model to trim and optimize redundant channels, yielding the pruned lightweight YOLOv8-TomatoStudent (YOLOv8-TS) model. In the third stage, using YOLOv8-TT as the teacher model and YOLOv8-TS as the student model, the detection accuracy of YOLOv8-TS was further enhanced through knowledge distillation, thus obtaining the YOLOv8-TomatoDetector (YOLOv8-TD) that considers both detection speed and accuracy. Experimental results indicate that YOLOv8-TD demonstrates significant advantages in parameter quantity and model complexity, with only 0.35 M Params and 4.4G GFlops, which are respectively reduced by 96.86 % and 84.51 % compared with baseline model YOLOv8s. In addition, YOLOv8-TD achieved a 1.4 % increase in mAP, and the detection speed of 354f/s satisfies real-time requirements. The model proposed in this study can swiftly and accurately detect dense small tomato targets, reducing the deployment cost and difficulty of the robot vision system.

Improved Small Object Detection Algorithm CRL-YOLOv5.

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.

Text-Guided Multi-Class Multi-Object Tracking for Fine-Grained Maritime Rescue

The rapid development of remote sensing technology has provided new sources of data for marine rescue and has made it possible to find and track survivors. Due to the requirement of tracking multiple survivors at the same time, multi-object tracking (MOT) has become the key subtask of marine rescue. However, there exists a significant gap between fine-grained objects in realistic marine rescue remote sensing data and the fine-grained object tracking capability of existing MOT technologies, which mainly focuses on coarse-grained object scenarios and fails to track fine-grained instances. Such a gap limits the practical application of MOT in realistic marine rescue remote sensing data, especially when rescue forces are limited. Given the promising fine-grained classification performance of recent text-guided methods, we delve into leveraging labels and attributes to narrow the gap between MOT and fine-grained maritime rescue. We propose a text-guided multi-class multi-object tracking (TG-MCMOT) method. To handle the problem raised by fine-grained classes, we design a multi-modal encoder by aligning external textual information with visual inputs. We use decoding information at different levels, simultaneously predicting the category, location, and identity embedding features of objects. Meanwhile, to improve the performance of small object detection, we also develop a data augmentation pipeline to generate pseudo-near-infrared images based on RGB images. Extensive experiments demonstrate that our TG-MCMOT not only performs well on typical metrics in the maritime rescue task (SeaDronesSee dataset), but it also effectively tracks open-set categories on the BURST dataset. Specifically, on the SeaDronesSee dataset, the Higher Order Tracking Accuracy (HOTA) reached a score of 58.8, and on the BURST test dataset, the HOTA score for the unknown class improved by 16.07 points.

Small Object Detection Method Based on Global Multi-Level Perception and Dynamic Region Aggregation

Small Object Detection Method Based on Global Multi-Level Perception and Dynamic Region Aggregation

Lightweight Detection Algorithm for Air-to-Ground Threat Object

Abstract In air-to-ground threat object detection, the typically strong concealment, small size, and high movement speed of threat objects often lead to challenges such as missed detections, low accuracy, and an excessive number of network parameters. An efficient lightweight detection algorithm for air-to-ground threat objects is proposed, built upon the YOLOv7-tiny framework. To address the issue of large network parameters, lightweight modules ELAN-GS and Slim-Neck are employed. Additionally, to refine the detection precision for small objects, the NWD metric and NWD loss function are introduced. Results from experiments on the DOTA dataset highlight that, compared with the original network, the proposed algorithm achieves a 33.2% reduction in parameters and a 26.3% reduction in computational cost. The small object accuracy, mAPs@0.5:0.95, is 23.7%, which is a 4.4% enhancement relative to the original network. The significant reduction in parameters and computational cost, along with the substantial improvement in small object detection accuracy, demonstrate the effectiveness of the proposed algorithm.

RSDM: Research of Rotation and Small Object Detection Models for Remote Sensing Images

Abstract Instance objects captured in aerial imagery display a range of orientations and exhibit significant scaling differences in their dimensions. Recent researches mainly address the above problems in terms of label assignment strategies and extended backbone spatial receptive fields, however, the former may still introduce lower quality samples, while the latter usually introduces considerable background noise. In this paper, the proposed Multiscale Convolutional Kernels Combined with Bi-Level Routing Attention for backbone Feature Extraction Network (MKBANet) employs multi-scale convolutional kernels to extract feature information at different scales, capture local context information, and combine with a Bi-Level Routing Attention (BRA) to capture remote context information and reduce computational complexity. In addition, an adaptive constrained oriented point set strategy (ACOPSS) is proposed. The adaptive point set constraint strategy is introduced in training, and then the quality metric function is utilized to select the top k high-quality directed point set samples in training, and finally the outliers are penalized. 81.23% mAP on DOTA dataset, the efficacy of the introduced approach is demonstrated.

SONet: A Small Object Detection Network for Power Line Inspection Based on YOLOv8

SONet: A Small Object Detection Network for Power Line Inspection Based on YOLOv8

FSIC: Frequency-Separated Image Compression for Small Object Detection

FSIC: Frequency-Separated Image Compression for Small Object Detection

YOLOv5_mamba: unmanned aerial vehicle object detection based on bidirectional dense feedback network and adaptive gate feature fusion

Addressing the problem that the object size in Unmanned Aerial Vehicles (UAVs) aerial images is too small and contains limited feature information, leading to existing detection algorithms having less than ideal performance in small object detection, we propose a UAV aerial object detection system named YOLv5_mamba based on bidirectional dense feedback network and adaptive gate feature fusion. This paper improves the You Only Look Once Version 5 (YOLOv5) algorithm by firstly introducing the Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module from YOLOv8 into the backbone network to enhance the feature extraction capability of the backbone network. Furthermore, the mamba module and C2f module are introduced to construct a bidirectional dense feedback network to enhance the transfer of contextual information in the neck part. Thirdly, an adaptive gate feature fusion network is proposed to improve the head part of YOLOv5 and enhance its final detection capability. Experimental results on the public UAV aerial dataset VisDrone2019 demonstrate that the proposed algorithm improves the detection accuracy by 9.3% compared to the original YOLOv5 baseline network, showing better detection performance for small objects. For the UCAS_AOD dataset, the proposed algorithm outperforms YOLOv5-s by 9%. In the case of the DIOR dataset, the proposed algorithm exceeds YOLOv5-s by 12%.

YOLO‐RSFM: An efficient road small object detection method

AbstractTo tackle challenges in road multi‐object detection, such as object occlusion, small object detection, and multi‐scale object detection difficulties, a new YOLOv8n‐RSFM structure is proposed. The key improvement of this structure lies in the introduction of the transformer decoder head, which optimizes the matching between the ground truth and predicted boxes, thereby effectively addressing issues of object overlap and multi‐scale detection. Additionally, a small object detection layer is incorporated to retain crucial information beneficial for detecting small objects, significantly improving the detection accuracy for small targets. To enhance learning capacity and reduce redundant computations, the FasterNet backbone is employed to replace CSPDarknet53, thus accelerating the training process. Finally, the INNER‐MPDIoU loss function is introduced to replace the original algorithm's complete IoU to accelerate the convergence and obtain more accurate regression results. A series of experiments were conducted on different datasets. The experimental results show that the proposed model YOLOv8N‐RSFM outperforms the original model YOLOv8n in small target detection. On the VisDrone, TinyPerson, and VSCrowd datasets, the mean accuracy percentage improved by 7.9%, 12.3%, and 4.5%, respectively.