SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection

To accurately detect multi-scale remote sensing objects in complex backgrounds, we propose a novel transformer-based adaptive object detection method. The backbone network of the method is a dual attention vision transformer network that utilizes spatial window attention and channel group attention to capture feature interactions between different objects in complex scenes. We further design an adaptive path aggregation network. In the designed network, CBAM (Convolutional Block Attention Module) is utilized to suppress background information in the fusion paths of different-level feature maps, and new paths are introduced to fuse same-scale feature maps to increase the feature information of the feature maps. The designed network can provide more effective feature information and improve the feature representation capability. Experiments conducted on the three datasets of RSOD, NWPU VHR-10, and DIOR show that the mAP of our method is 96.9%, 96.6%, and 81.7%, respectively, which outperforms compared object detection methods. The experimental results show that our method can detect remote-sensing objects better.

There has been no prior general purpose brain-mind model for multiple events in complex backgrounds. I first discuss that although the age of brain-mind seems to have arrived, the current infrastructure does not well fit the need of research and peer-review for the challenging and important subject of brain-mind. Then, I present a general purpose model of the brain-mind, called the Epigenetic Developer (ED) network model. The model proposes five necessary “chunks” for the brain picture: development, architecture, area, space and time. The development chunk means that any practical brain, natural or artificial, needs to autonomously develop through interactions with the natural environments without any previously given set of tasks. The architecture chunk handles (1) multiple objects in complex backgrounds; (2) reasoning under abstract contexts; (3) multiple sensory modalities and multiple motor modalities and their integration. The area chunk addresses the issue of feature development and area representation, without rigidly specifying what each neuron does. The space chunk deals with spatial attention to individual objects in complex backgrounds, to satisfy the invariance and specificity criteria for type, location, and other concepts. The time chunk indicates that the brain uses its intrinsic spatial mechanisms to deals with time, without dedicated temporal components. The model copes with temporal contexts of events, to satisfy invariance and specificity criteria for time warping, time duration, temporal attention, and long temporal length. The theory and mechanisms are presented and some related experimental results are summarized.

In practical human–computer interaction, a hand gesture recognition method based on improved YOLOv5 is proposed to address the problem of low recognition accuracy and slow speed with complex backgrounds. By replacing the CSP1_x module in the YOLOv5 backbone network with an efficient layer aggregation network, a richer combination of gradient paths can be obtained to improve the network’s learning and expressive capabilities and enhance recognition speed. The CBAM attention mechanism is introduced to filtering gesture features in channel and spatial dimensions, reducing various types of interference in complex background gesture images and enhancing the network’s robustness against complex backgrounds. Experimental verification was conducted on two complex background gesture datasets, EgoHands and TinyHGR, with recognition accuracies of mAP0.5:0.95 at 75.6% and 66.8%, respectively, and a recognition speed of 64 FPS for 640 × 640 input images. The results show that the proposed method can recognize gestures quickly and accurately with complex backgrounds, and has higher recognition accuracy and stronger robustness compared to YOLOv5l, YOLOv7, and other comparative algorithms.

Existing DEtection TRansformer-based (DETR) object detection methods have been widely applied to standard object detection tasks, but still face numerous challenges in detecting small objects. These methods frequently miss the fine details of small objects and fail to preserve global context, particularly under scale variation or occlusion. The resulting feature maps lack sufficient spatial and structural information. Moreover, some DETR-based models specifically designed for small object detection often have poor generalization capabilities and are difficult to adapt to datasets with diverse object scales and complex backgrounds. To address these issues, this paper proposes a novel object detection model—small object detection with efficient multi-scale collaborative attention and depth feature fusion based on DETR (ED-DETR)—which consists of three core modules: an efficient multi-scale collaborative attention mechanism (EMCA), DepthPro, a zero-shot metric monocular depth estimation model, and an adaptive feature fusion module for depth maps and feature maps. Specifically, EMCA extends the single-space attention mechanism in efficient multi-scale attention (EMA) to a composite structure of parallel spatial and channel attention, enhancing ED-DETR’s ability to express features collaboratively in both spatial and channel dimensions. DepthPro generates depth maps to extract depth information. The adaptive feature fusion module integrates depth information with RGB visual features, improving ED-DETR’s ability to perceive object position, scale, and occlusion. The experimental results show that ED-DETR achieves the current best 33.6% mAP on the AI-TOD-V2 dataset, which predominantly contains tiny objects, outperforming previous CNN-based and DETR-based methods, and shows excellent generalization performance on the VisDrone and COCO datasets.

For the routine target detection algorithm in the underwater complex environment to obtain the image of the existence of blurred images, complex background and other phenomena, leading to difficulties in model feature extraction, target miss detection and other problems. Meanwhile, an improved YOLOv7 model is proposed in order to improve the accuracy and real-time performance of the underwater target detection model. The improved model is based on the single-stage target detection model YOLOv7, incorporating the CBAM attention mechanism in the model, so that the feature information of the detection target is weighted and enhanced in the spatial dimension and the channel dimension, capturing the local relevance of feature information, making the model more focused on target feature information, improved detection accuracy, and using the SPPFCSPC module, reducing the computational effort of the model while keeping the model perceptual field unchanged, improved inference speed of the model. After a large number of comparison experiments and ablation experiments, it is proved that our proposed ACFP-YOLO algorithm model has higher detection accuracy compared with Efficientdet, Faster-RCNN, SSD, YOLOv3, YOLOv4, YOLOv5 models and the latest YOLOv7 model, and is more accurate for target detection tasks in complex underwater environments advantages.

Due to the influence of the shooting angle of view and the flight height, the images taken by UAV often have complex backgrounds and contain a large number of small and unevenly distributed objects. In order to solve the problem that it is difficult to accurately locate and recognize small objects in UAV images under complex backgrounds, this paper proposes an multi-scale feature fusion algorithm D-A-FS SSD (Dilated-Attention-Feature Fusion SSD) based on the combination of dilated convolution and attention mechanism. In the process of feature extraction, the receptive field of the feature is expanded through the dilated convolution, which improves the network's feature expression of object distribution and scale difference information. And a attention network is used in our method to effectively suppresse the background information. In the multi-scale detection stage, our method fuses the low-level feature map responsible for detecting small objects with the high-level feature map which have much higher semantic information to improve the recognition accuracy of small objects. Experimental results show that our method effectively improves the accuracy of UAV image object detection.

Tracking multiple moving objects by binary object forest segmentation

Panoramic image object detection has significant applications in autonomous driving, robotic navigation, and security monitoring. However, most current object detection algorithms are trained on pinhole images and cannot be directly applied to panoramic images, which have a large field-of-view (FOV) and distortion. Additionally, research on panoramic image object detection lacks dedicated dataset support, and these images face challenges such as target distortion, occlusion, and multi-scale variations. Existing methods for panoramic image object detection have not yielded satisfactory performance. To address these issues, we propose PanoDetNet, an object detection model based on YOLOv7. We introduce two new modules: the Multi-Scale Feature Fusion (MSFF) module and the Adaptive Panoramic Feature Attention (APFA) module. The MSFF module enhances detection precision for targets of different scales by fusing feature maps of various sizes, while also reducing the number of parameters and simplifying the model structure. The APFA module adaptively addresses the distortion in panoramic images, improving the model’s ability to locate and recognize objects in complex backgrounds and under occlusion. We trained PanoDetNet on our self-built panoramic image object detection dataset, PanoDet. This dataset was collected using a self-developed panoramic camera and manually annotated with the Labelme tool. Experimental results show that PanoDetNet achieves mAP@.5, mAP@.5:.95, and accuracy scores of 95.3%, 75.2%, and 94.1%, respectively. These results represent improvements of 1.6%, 2.5%, and 3.3% over YOLOv7. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/github98317/PanoDetNet</uri> .

Change detection (CD) is an important remote sensing (RS) data analysis technology. Existing remote sensing change detection (RS-CD) technologies cannot fully consider situations where pixels between bitemporal images do not correspond well on a one-to-one basis due to factors such as seasonal changes and lighting conditions. Existing networks construct two identical feature extraction branches through convolution, which share weights. The two branches work independently and do not merge until the feature mapping is sent to the decoder head. This results in a lack of feature information interaction between the two images. So, directing attention to the change area is of research interest. In complex backgrounds, the loss of edge details is very important. Therefore, this paper proposes a new CD algorithm that extracts multi-scale feature information through the backbone network in the coding stage. According to the task characteristics of CD, two submodules (the Feature Interaction Module and Detail Feature Guidance Module) are designed to make the feature information between the bitemporal RS images fully interact. Thus, the edge details are restored to the greatest extent while fully paying attention to the change areas. Finally, in the decoding stage, the feature information of different levels is fully used for fusion and decoding operations. We build a new CD dataset to further verify and test the model’s performance. The generalization and robustness of the model are further verified by using two open datasets. However, due to the relatively simple construction of the model, it cannot handle the task of multi-classification CD well. Therefore, further research on multi-classification CD algorithms is recommended. Moreover, due to the high production cost of CD datasets and the difficulty in obtaining them in practical tasks, future research will look into semi-supervised or unsupervised related CD algorithms.

In recent years, deep learning-based remote sensing object detection has achieved remarkable progress, yet the detection of tiny objects remains a significant challenge. Tiny objects in remote sensing images typically occupy only a few pixels, resulting in low contrast, poor resolution, and high sensitivity to localization errors. Their diverse scales and appearances, combined with complex backgrounds and severe class imbalance, further complicate the detection tasks. Conventional spatial feature extraction methods often struggle to capture the discriminative characteristics of tiny objects, especially in the presence of noise and occlusion. To address these challenges, we propose a frequency-aware attention-based tiny-object detection network with two plug-and-play modules that leverage frequency-domain information to enhance the targets. Specifically, we introduce a Multi-Scale Frequency Feature Enhancement Module (MSFFEM) to adaptively highlight the contour and texture details of tiny objects while suppressing background noise. Additionally, a Channel Attention-based RoI Enhancement Module (CAREM) is proposed to selectively emphasize high-frequency responses within RoI features, further improving object localization and classification. Furthermore, to mitigate sample imbalance, we employ multi-directional flip sample augmentation and redundancy filtering strategies, which significantly boost detection performance for few-shot categories. Extensive experiments on public object detection datasets, i.e., AI-TOD, VisDrone2019, and DOTA-v1.5, demonstrate that the proposed FANet consistently improves detection performance for tiny objects, outperforming existing methods and providing new insights into the integration of frequency-domain analysis and attention mechanisms for robust tiny-object detection in remote sensing applications.

With the development of remote sensing technology, remote sensing object detection has been widely applied in various fields, but it still faces some thorny challenges, such as the following: 1) the complexity of object scale changes in remote sensing images makes it difficult to improve the performance of small object detection and 2) remote sensing images have complex backgrounds and densely arranged small and weak objects, which pose a serious problem of feature interference. To alleviate these challenges, we propose an end-to-end adaptive feature separation network called AFSNet, which includes a scale-aware module (SAM) and a class-aware module (CAM). The SAM mainly enables feature maps of different resolutions to detect objects of different scales. Shallow feature maps mainly suppress the features of large objects they contain to focus on small object detection, while deep feature maps increase the detailed features of large objects they contain to focus on large object detection. The CAM is mainly used to distinguish the features in the feature map by category, separating the features of different categories into different channels, thus mitigating the problem of inter class feature interference, and blocking background interference. The effectiveness of this article has been proven on the NWPU VHR-10, IPIU-M, DIOR, and DOTA2.0 datasets. It can be widely applied in civilian, military, and other fields. Through experimental verification, our AFSNet achieved 97.70% mAP on the NWPU VHR-10 dataset, 78.9% mAP on the DIOR dataset, and 58.22% mAP on the DOTA2.0 dataset. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Xidian-AIGroup190726/AFSNet</uri>.

The monitoring of the road freight vehicles traffic on urban roads relies on the work of professionals who need to be fully aware of the traffic on a specific road, and thereby identify the presence of these vehicles in complex scenes. Automatic vehicle identification in video shots may add value to the city life quality, due to congestion diminishment and traffic security improvement. In this research, a model able to identify and segregate road freight vehicles on urban roads is proposed, by means of video analysis and processing, that were recorded by cameras positioned at different angles on highways accesses on urban roads. The system was developed using MATLAB. Techniques related to detecting, targeting and processing steps have been used to identify objects in complex backgrounds. At the end of the work, the system is applied to several scenarios that have been filmed in the city of So Carlos -SP and the results are discussed.

Nowadays, moving object detection in sequence images has become a hot topic in computer vision research, and has a very wide range of practical applications in many fields of military and daily life. In this paper, fast detection of moving objects in complex background is studied, and fast detection methods for moving objects in static and dynamic scenes are proposed respectively. Firstly, based on image preprocessing, aiming at the difficulty of feature extraction of moving targets in low illumination at night, Gamma change is used to process. Secondly, for the fast detection of moving objects in static scenes, this paper designs a detection method combining background difference and edge frame difference. Finally, aiming at the fast detection of moving objects in dynamic scenes, a feature matching detection method based on the SIFT algorithm is designed in this paper. Simulation experiments show that the method designed in this paper has good detection performance.

Efficient waste detection is crucial for environmental sustainability, yet existing models struggle with finegrained objects in complex backgrounds, such as those in the TACO dataset. This paper proposes an attention-centric approach using YOLOv12n to balance detection accuracy and real-time performance. Experimental results on the TACO dataset demonstrate that the proposed YOLOv12n achieves a mean Average Precision (mAP50 ) of 0.376 with an end-to-end inference speed of 94.33 FPS on an NVIDIA RTX 5060 GPU. Ablation studies reveal that removing the Area-Attention ( A2 ) module leads to a significant performance drop, with mAP50 plummeting from 0.376 to 0.148. Furthermore, compared to the YOLOv8n model with a plug-in CBAM module (46.18 FPS, 0.310 mAP), the native attention-centric architecture of YOLOv12n provides a significant increase in inference speed and superior feature localization. This research confirms that a native attention-based design is more effective for realtime fine-grained waste detection than traditional modular additions.

With the rapid advancement of deep learning technology, the problem of object detection in the field of remote sensing has received increasing attention. However, existing methods still face challenges when dealing with remote sensing image detection tasks, such as insufficient accuracy in small object recognition, an imbalance between recognition precision and detection efficiency, and difficulties in handling multi-scale variations. Based on the above problems, this letter proposes a lightweight and efficient remote sensing image object detector MERS-Net. Firstly, we propose an enhanced feature extraction module SCFE based on sub-channel mixing to improve the feature extraction capability of the model under complex scale transformation. Secondly, we incorporated the WaveletPool module along with the GSConv and VoVGSCSP modules from Slimneck into the model to reduce computational parameters. Finally, we designed a lightweight detection head MRF-Detect based on the parameter sharing mechanism to improve the recognition capability of the model under limited hardware conditions. We verified the effectiveness of MERS-Net on the public datasets DOTA, AI-TOD, and DIOR datasets. Compared with current mainstream algorithms such as YOLOv11, the average accuracy (mAP) is improved by 2.6%, 2.5% and 2.7%, respectively. At the same time, the parameter quantity and GFLOPs are significantly reduced, showing its superior detection performance.