Object Detection Network Research Articles

YOLO-SGF: Lightweight network for object detection in complex infrared images based on improved YOLOv8

The current mainstream object detection networks perform well in RGB visible images, but they require high computational resource and degrade in performance when applied to low-resolution infrared images. To address above issues, we propose a lightweight algorithm YOLO-SGF based on you-only-look-once version8 (YOLOv8). Firstly, the lightweight cross-scale feature map fusion network GCFVoV designed as neck to solve poor detection accuracy and maintain low complexity in lightweight networks. And a lightweight GCVF module in GCFVoV neck uses GSConv and Conv to process deep and shallow features respectively, which maximally preserves implicit connections between each channel and integrates multi-scale features. Secondly, we utilize ShuffleNetV2-block1 in combination with C2f for feature extraction, making the algorithm more lightweight and effectively. Finally, we propose the FIMPDIoU loss function, which focuses on overlooked objects in complex backgrounds and adjusts the prediction boxes using ratios specific to different sizes of objects. Compared with YOLOv8 in our infrared dataset, YOLO-SGF reduces the computational space complexity by 50 % and time complexity by 42 %, increases FPS32 by 36.3 % and improves mAP@0.5 ∼ 0.95 by 1.1 % in object detection. Our algorithm enhances the capability of object detection in infrared images especially in nighttime, low light, and occluded conditions. YOLO-SGF enables deployment on embedded edge devices with limited computing power, and provides a new idea for lightweight networks.

Lightweight Single-Stage Ship Object Detection Algorithm for Unmanned Surface Vessels Based on Improved YOLOv5.

Object detection is applied extensively in various domains, including industrial manufacturing, road traffic management, warehousing and logistics, and healthcare. In ship object detection tasks, detection networks are frequently deployed on devices with limited computational resources, e.g., unmanned surface vessels. This creates a need to balance accuracy with a low parameter count and low computational load. This paper proposes an improved object detection network based on YOLOv5. To reduce the model parameter count and computational load, we utilize an enhanced ShuffleNetV2 network as the backbone. In addition, a split-DLKA module is devised and implemented in the small object detection layer to improve detection accuracy. Finally, we introduce the WIOUv3 loss function to minimize the impact of low-quality samples on the model. Experiments conducted on the SeaShips dataset demonstrate that the proposed method reduces parameters by 71% and computational load by 58% compared to YOLOv5s. In addition, the proposed method increases the mAP@0.5 and mAP@0.5:0.95 values by 3.9% and 3.3%, respectively. Thus, the proposed method exhibits excellent performance in both real-time processing and accuracy.

Foreign object detection in urban rail transit based on deep differentiation segmentation neural network

With the increasing scale of urban rail transit, foreign object intrusion has become a significant operational safety hazard in urban rail transit. Although the laser-based automatic foreign object detection system has advantages such as long-distance detection and insensitivity to light changes, it has drawbacks such as large blind spots and low visualization. In response to the problems existing in laser detection systems, we proposed a novel video-based deep differentiation segmentation neural network for foreign object detection. Firstly, the foreign object detection is transformed into a binary classification problem, and the foreign object is determined as the image's foreground using image segmentation principles. Secondly, build a deep segmentation network based on deep convolution. Finally, perform morphological operations and threshold judgment on the foreground segmentation image to filter out the final detection results. To improve the detection effect, we reduced the impact of airflow disturbance by sampling and calculating the average background image. At the same time, the channel attention model and spatial attention model are added to the deep differentiation neural network. Collecting real data on subway platforms for experiments shows that the proposed method has a detection accuracy of 95.8 %, which is superior to traditional detection methods and recent image segmentation neural networks.

Cross-stage feature fusion and efficient self-attention for salient object detection

Salient Object Detection (SOD) approaches usually aggregate high-level semantics with object details layer by layer through a pyramid fusion structure. However, the progressive feature fusion mechanism may lead to gradually dilution of valuable semantics and prediction accuracy. In this work, we propose a Cross-stage Feature Fusion Network (CFFNet) for salient object detection. CFFNet consists of a Cross-stage Semantic Fusion Module (CSF), a Feature Filtering and Fusion Module (FFM), and a progressive decoder to tackle the above problems. Specifically, to alleviate the semantics dilution problem, CSF concatenates different stage backbone features and extracts multi-scale global semantics using transformer blocks. Global semantics are then distributed to corresponding backbone stages for cross-stage semantic fusion. The FFM module implements efficient self-attention-based feature fusion. Different from regular self-attention which has quadratic computational complexity. Finally, a progressive decoder is adopted to refine saliency maps. Experimental results demonstrate that CFFNet outperforms state-of-the-arts on six SOD datasets.

A Lightweight Crop Pest Detection Method Based on Improved RTMDet

To address the issues of low detection accuracy and large model parameters in crop pest detection in natural scenes, this study improves the deep learning object detection model and proposes a lightweight and accurate method RTMDet++ for crop pest detection. First, the real-time object detection network RTMDet is utilized to design the pest detection model. Then, the backbone and neck structures are pruned to reduce the number of parameters and computation. Subsequently, a shortcut connection module is added to the classification and regression branches, respectively, to enhance its feature learning capability, thereby improving its accuracy. Experimental results show that, compared to the original model RTMDet, the improved model RTMDet++ reduces the number of parameters by 15.5%, the computation by 25.0%, and improves the mean average precision by 0.3% on the crop pest dataset IP102. The improved model RTMDet++ achieves a mAP of 94.1%, a precision of 92.5%, and a recall of 92.7% with 4.117M parameters and 3.130G computations, outperforming other object detection methods. The proposed model RTMDet++ achieves higher performance with fewer parameters and computations, which can be applied to crop pest detection in practice and aids in pest control research.

Progressive cross-level fusion network for RGB-D salient object detection

Depth maps can provide supplementary information for salient object detection (SOD) and perform better in handling complex scenes. Most existing RGB-D methods only utilize deep cues at the same level, and few methods focus on the information linkage between cross-level features. In this study, we propose a Progressive Cross-level Fusion Network (PCF-Net). It ensures the cross-flow of cross-level features by gradually exploring deeper features, which promotes the interaction and fusion of information between different-level features. First, we designed a Cross-Level Guide Cross-Modal Fusion Module (CGCF) that utilizes the spatial information of upper-level features to suppress modal feature noise and to guide lower-level features for cross-modal feature fusion. Next, the proposed Semantic Enhancement Module (SEM) and Local Enhancement Module (LEM) are used to further introduce deeper features, enhance the high-level semantic information and low-level structural information of cross-modal features, and use self-modality attention refinement to improve the enhancement effect. Finally, the multi-scale aggregation decoder mines enhanced feature information in multi-scale spaces and effectively integrates cross-scale features. In this study, we conducted numerous experiments to demonstrate that the proposed PCF-Net outperforms 16 of the most advanced methods on six popular RGB-D SOD datasets.

Drone-DETR: Efficient Small Object Detection for Remote Sensing Image Using Enhanced RT-DETR Model.

Performing low-latency, high-precision object detection on unmanned aerial vehicles (UAVs) equipped with vision sensors holds significant importance. However, the current limitations of embedded UAV devices present challenges in balancing accuracy and speed, particularly in the analysis of high-precision remote sensing images. This challenge is particularly pronounced in scenarios involving numerous small objects, intricate backgrounds, and occluded overlaps. To address these issues, we introduce the Drone-DETR model, which is based on RT-DETR. To overcome the difficulties associated with detecting small objects and reducing redundant computations arising from complex backgrounds in ultra-wide-angle images, we propose the Effective Small Object Detection Network (ESDNet). This network preserves detailed information about small objects, reduces redundant computations, and adopts a lightweight architecture. Furthermore, we introduce the Enhanced Dual-Path Feature Fusion Attention Module (EDF-FAM) within the neck network. This module is specifically designed to enhance the network's ability to handle multi-scale objects. We employ a dynamic competitive learning strategy to enhance the model's capability to efficiently fuse multi-scale features. Additionally, we incorporate the P2 shallow feature layer from the ESDNet into the neck network to enhance the model's ability to fuse small-object features, thereby enhancing the accuracy of small object detection. Experimental results indicate that the Drone-DETR model achieves an mAP50 of 53.9% with only 28.7 million parameters on the VisDrone2019 dataset, representing an 8.1% enhancement over RT-DETR-R18.

A lightweight Transformer-based visual question answering network with Weight-Sharing Hybrid Attention

Recent advances show that Transformer-based models and object detection-based models play an indispensable role in VQA. However, object detection-based models have significant limitations due to their redundant and complex detection box generation process. In contrast, Visual and Language Pre-training (VLP) models can achieve better performance, but require high computing power. To this end, we present Weight-Sharing Hybrid Attention Network (WHAN), a lightweight Transformer-based VQA model. In WHAN, we replace the object detection network with Transformer encoder and use LoRA to solve the problem that the language model cannot adapt to interrogative sentences. We propose Weight-Sharing Hybrid Attention (WHA) module with parallel residual adapters, which can significantly reduce the trainable parameters of the model and we design DWA and BVA modules that can allow the model to perform attention operations from different scales. Experiments on VQA-v2, COCO-QA, GQA, and CLEVR datasets show that WHAN achieves competitive performance with far fewer trainable parameters.

Deformable Capsules for Object Detection

Capsule networks promise significant benefits over convolutional neural networks (CNN) by storing stronger internal representations and routing information based on the agreement between intermediate representations’ projections. Despite this, their success has been limited to small‐scale classification datasets due to their computationally expensive nature. Though memory‐efficient, convolutional capsules impose geometric constraints that fundamentally limit the ability of capsules to model the pose/deformation of objects. Further, they do not address the bigger memory concern of class capsules scaling up to bigger tasks such as detection or large‐scale classification. Herein, a new family of capsule networks, deformable capsules (DeformCaps), is introduced to address object detection problem in computer vision. Two new algorithms associated with our DeformCaps, a novel capsule structure (SplitCaps), and a novel dynamic routing algorithm (SE‐Routing), which balance computational efficiency with the need for modeling a large number of objects and classes, are proposed. This has never been achieved with capsule networks before. The proposed methods efficiently scale up to create the first‐ever capsule network for object detection in the literature. The proposed architecture is a one‐stage detection framework and it obtains results on microsoft common objects in context which are on par with state‐of‐the‐art one‐stage CNN‐based methods, while producing fewer false‐positive detection, generalizing to unusual poses/viewpoints of objects.

Saliency and edge features-guided end-to-end network for salient object detection

The rapid development of the Vision Transformer backbones has enabled the capture of feature information with global dependencies, leading to excellent performance in salient object detection tasks. However, it fails to adequately emphasize fine local features of edges, resulting in coarse and blurry edges in the final output. Therefore, this paper proposes a set prediction method for salient object detection. The approach enables the model to simultaneously consider both saliency (salient object) and edge features, achieving an end-to-end edge feature fusion. There is no need to design multiple complex branch structures and multiple training as with other methods. The model integrates random edge neighborhood sampling to enhance the recognition accuracy of local edge features in images. This approach addresses the weak perception of local features by Transformers and the issue encountered during actual training, where edge pixels typically occupy a much smaller proportion of the image compared to the background, resulting in insufficient learning of edge features by the model. The proposed end-to-end model in this paper fuses multiple features, including edge and salient objects. They are extracted within a unified framework while simultaneously outputting edge and salient object maps. Experimental results on six public datasets show that the proposed method significantly improves model performance benchmarks for detecting salient objects.

LP-YOLO: A Lightweight Object Detection Network Regarding Insect Pests for Mobile Terminal Devices Based on Improved YOLOv8

LP-YOLO: A Lightweight Object Detection Network Regarding Insect Pests for Mobile Terminal Devices Based on Improved YOLOv8

FCFIG-Net: feature complementary fusion and information-guided network for RGB-D salient object detection

FCFIG-Net: feature complementary fusion and information-guided network for RGB-D salient object detection

Single-stage zero-shot object detection network based on CLIP and pseudo-labeling

Single-stage zero-shot object detection network based on CLIP and pseudo-labeling

Green fruit detection methods: Innovative application of camouflage object detection and multilevel feature mining

Accurate detection of object fruits is essential for optimizing picking efficiency and predicting fruit yields. However, detecting early-stage ripening or green fruits, especially in complex fields, poses significant challenge due to their similarity to green leaves. This study introduces the TEAVit model, a novel camouflage object detection network specifically tailored for identifying green tomatoes in intricate agricultural environments. TEAVit incorporates a texture-edge-awareness module (TEAM) to enhance the extraction ability of texture feature by combining high-level and low-level features, an edge-guided feature module (EFM) to address background complexities and occlusions, and a context-aggregation module (CAM) to leverage contextual semantics. Experimental validation results demonstrate that the S-measure, E-measure, and F-measure performance metrics all exceed 90% on the tomato dataset, accompanied by a mean absolute error of 0.0245. These findings underpinned the effectiveness of the proposed green fruit camouflage object detection algorithm in offering new insights for agricultural target localization.

Deep Edge-Based Fault Detection for Solar Panels.

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU.

TS-BEV: BEV object detection algorithm based on temporal-spatial feature fusion

In order to accurately identify occluding targets and infer the motion state of objects, we propose a Bird’s-Eye View Object Detection Network based on Temporal-Spatial feature fusion (TS-BEV), which replaces the previous multi-frame sampling method by using the cyclic propagation mode of historical frame instance information. We design a new Temporal-Spatial feature fusion attention module, which fully integrates temporal information and spatial features, and improves the inference and training speed. In response to realize multi-frame feature fusion across multiple scales and views, we propose an efficient Temporal-Spatial deformable aggregation module, which performs feature sampling and weighted summation from multiple feature maps of historical frames and current frames, and makes full use of the parallel computing capabilities of GPUs and AI chips to further improve efficiency. Furthermore, in order to solve the lack of global inference in the context of temporal-spatial fusion BEV features and the inability of instance features distributed in different locations to fully interact, we further design the BEV self-attention mechanism module to perform global operation of features, enhance global inference ability and fully interact with instance features. We have carried out extensive experimental experiments on the challenging BEV object detection nuScenes dataset, quantitative results show that our method achieves excellent performance of 61.5% mAP and 68.5% NDS in camera-only 3D object detection tasks, and qualitative results show that TS-BEV can effectively solve the problem of 3D object detection in complex traffic background with lack of light at night, with good robustness and scalability.

D2Net: discriminative feature extraction and details preservation network for salient object detection

D2Net: discriminative feature extraction and details preservation network for salient object detection

LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle

Autonomous execution of tasks by unmanned aerial vehicles (UAVs) relies heavily on object detection. However, object detection in most images presents challenges such as complex backgrounds, small targets, and obstructions. Additionally, the limited computing speed and memory of the UAV processor affects the accuracy of conventional object detection algorithms. This paper proposes LUD-You Only Look Once (YOLO), a small and lightweight object detection algorithm for UAVs based on YOLOv8. The proposed algorithm introduces a new multiscale feature fusion mode that solves the degradation in feature propagation and interaction through the introduction of upsampling in the feature pyramid network and the progressive feature pyramid network. The application of the dynamic sparse attention mechanism in the Cf2 module achieves flexible computing allocation and content awareness. Furthermore, the proposed model is optimized to be sparse and lightweight, making it possible to deploy on UAV edge devices. Finally, the effectiveness and superiority of LUD-YOLO were verified on the VisDrone2019 and UAVDT datasets. The results of ablation and comparison experiments show that compared with the original algorithm, LUDY-N and LUDY-S have shown excellent performance in various evaluation indexes, indicating that the proposed improvement strategies make the model have better robustness and generalization. Moreover, compared with multiple other popular competitors, the proposed improvement strategies enable LUD-YOLO to have the best overall performance, providing an effective solution for UAVs object detection while balancing model size and detection accuracy.

A Lightweight Convolutional Neural Network for Salient Object Detection

A Lightweight Convolutional Neural Network for Salient Object Detection

Global Context Relation-Guided Feature Aggregation Network for Salient Object Detection in Optical Remote Sensing Images

With the rapid development of deep neural networks, salient object detection has achieved great success in natural images. However, detecting salient objects from optical remote sensing images still remains a challenging task due to the diversity of object types, scale, shape and orientation variations, as well as cluttered backgrounds. Therefore, it is impractical to directly leverage methods designed for natural images to detect salient objects in optical remote sensing images. In this work, we present an end-to-end deep neural network for salient object detection in optical remote sensing images via global context relation-guided feature aggregation. Since the objects in remote sensing images often have a scattered distribution, we design a global context relation module to capture the global relationships between different spatial positions. In order to effectively integrate low-level appearance features as well as high-level semantic features for enhancing the final performance, we develop a feature aggregation module with the global context relation information as guidance and embed it into the backbone network to refine the deep features in a progressive manner. Instead of using traditional binary cross entropy as a training loss which treats all pixels equally, we design a weighted binary cross entropy to capture local surrounding information of different pixels. Extensive experiments on three public datasets are conducted to validate the efficiency of the proposed network and the results demonstrate that our proposed method consistently outperforms other competitors.