Improved YOLOv5 Network with CBAM for Object Detection Vision Drone

SummaryAs various algorithms and models spring up, object detection‐based deep learning for image analysis has become an established technology in the field of computer vision. Furthermore, object detection for high‐resolution videos derived from ubiquitous surveillance cameras draws many researchers' attention due to its practical significance and the challenge on detecting accuracy and speed. Existing object detection methods mainly resize and compress each frame in the video and then apply the detection algorithm, but with the method, the detection for small objects in dense scenes is far from satisfactory in terms of accuracy because small objects are reduced to pixel‐level sizes in compressed frame images. In order to fix this issue, we propose AROD, a parallel detection framework based on adaptive image cropping. Unlike the traditional first‐compression‐and‐then‐detection methods, AROD adopts adaptive image cropping for distributed parallel detection. In this way, AROD significantly improves the accuracy of small object detection in dense scenes with acceptable overhead. In our experimental evaluation, YOLOv2‐tiny model equipped with AROD reaches 29.29 FPS along with high detection accuracy. Experimental results verify that AROD ensures the high throughput of real‐time video analytics while maintaining high detection accuracy.

Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

To further improve the speed and accuracy of object detection, especially small targets and occluded objects, a novel and efficient detector named YOLO-ACN is presented. The detector model is inspired by the high detection accuracy and speed of YOLOv3, and it is improved by the addition of an attention mechanism, a CIoU (complete intersection over union) loss function, Soft-NMS (non-maximum suppression), and depthwise separable convolution. First, the attention mechanism is introduced in the channel and spatial dimensions in each residual block to focus on small targets. Second, CIoU loss is adopted to achieve accurate bounding box (BBox) regression. Besides, to filter out a more accurate BBox and avoid deleting occluded objects in dense images, the CIoU is applied in the Soft-NMS, and the Gaussian model in the Soft-NMS is employed to suppress the surrounding BBox. Third, to significantly reduce the parameters and improve the detection speed, standard convolution is replaced by depthwise separable convolution, and hard-swish activation function is utilized in deeper layers. On the MS COCO dataset and infrared pedestrian dataset KAIST, the quantitative experimental results show that compared with other state-of-the-art models, the proposed YOLO-ACN has high accuracy and speed in detecting small targets and occluded objects. YOLO-ACN reaches a mAP50 (mean average precision) of 53.8% and an APs (average precision for small objects) of 18.2% at a real-time speed of 22 ms on the MS COCO dataset, and the mAP for a single class on the KAIST dataset even reaches over 80% on an NVIDIA Tesla K40.

Object detection for mobile devices is meaningful especially in the field of IoT. Limited by computing power and network transmission, it's challenging to get high accuracy in mobile object detection. To solve this question, this article designs a system that enables high accuracy object detection running at 30fps for 720p videos. The system employs the object tracking technique, uses the caching technique, decouples the rendering pipeline from the offloading pipeline, and uses dynamic RoI encoding technique to get high detection accuracy. The result of the experiment shows that it can get 88% detection success rate. And it can also increase the detection accuracy by 17.7% and decrease the bandwidth by 52.6%.

Accurate object detection in unmanned aerial vehicle (UAV) images plays a crucial role in fields such as aviation, transportation, and agriculture. However, UAV images often contain a high proportion of small objects, and the limited resources of UAV platforms make it challenging for existing small object detection algorithms to balance detection performance and resource consumption. To address these issues, a lightweight small object detection algorithm called SRP-YOLO is proposed. Compared to the YOLOv8 network, the following improvements are made: first, a high-resolution detection head is designed; second, a receptive field attention convolution module tailored for small UAV objects is introduced to replace the standard convolution module, enhancing the detection capability for small objects and significantly improving their feature representation; Third, partial convolution is combined with C2f to replace some of the standard convolutions, fully leveraging the high-resolution information from shallow features. Finally, A normalized Gaussian Wasserstein Distance (NWD) metric is introduced to reduce the sensitivity of Intersection over Union (IoU) to minor positional deviations of small objects. On VisDrone-DET2019, SRP-YOLO reduces parameters while improving mAP 0.5 by 7.9%. Generalization tests on TinypersonV2 and RAIVD confirm significant gains in small object detection.

Mobile phone component object detection algorithm based on improved SSD

Recently, fast and accurate DNN object detectors such as YOLO and SSD have attracted considerable attention. However, it still takes far more time than real time processing when inputting a 4K video and becomes even more challenging when inputting multi-view 4K sports videos due to the massive amount of data and small objects. This paper presents a novel approach to significantly accelerate object detection. Observing the fact that object regions (including players and balls) are very sparse in sports videos, we crop the images to greatly reduce the processing areas using the temporal or view correlations. However, simple image cropping may worsen the accuracy if the object is too small to be detected. A further observation is that the detection can be improved by resizing the small objects properly. Our experimental results on two soccer matches of J1 League demonstrate that we can make the detection much faster with a high accuracy for small objects.

Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. Because of its low recognition accuracy, slow recognition speed and poor localization accuracy, the traditional algorithm cannot meet the requirements of automatic-harvesting robots. The increasingly evolving and powerful deep learning technology can effectively solve the above problems and has been widely used in the last few years. This work systematically summarizes and analyzes about 120 related literatures on the object detection and three-dimensional positioning algorithms of harvesting robots over the last 10 years, and reviews several significant methods. The difficulties and challenges faced by current fruit detection and localization algorithms are proposed from the aspects of the lack of large-scale high-quality datasets, the high complexity of the agricultural environment, etc. In response to the above challenges, corresponding solutions and future development trends are constructively proposed. Future research and technological development should first solve these current challenges using weakly supervised learning, efficient and lightweight model construction, multisensor fusion and so on.

With the development of deep convolutional neural networks (CNNs), the object detection accuracy has been greatly improved. But the performance of small object detection is still far from satisfactory, mainly because small objects are so tiny that the information contained in the feature map is limited. Existing methods focus on improving classification accuracy but still suffer from the limitation of bounding box prediction. To solve this issue, we propose a detection framework by generative and discriminative learning. First, a reconstruction generator network is designed to reconstruct the mapping from low frequency to high frequency for anchor box prediction. Then, a detector module extracts the regions of interest (ROIs) from generated results and implements a RoI-Head to predict object category and refine bounding box. In order to guide the reconstructed image related to the corresponding one, a discriminator module is adopted to tell from the generated result and the original image. Extensive evaluations on the challenging MS-COCO dataset demonstrate that our model outperforms most state-of-the-art models in detecting small objects, especially the reconstruction module improves the average precision for small object (APs) by 7.7%.

Drone-based object detection has been widely applied in ground object surveillance, urban patrol, and some other fields. However, the dramatic scale changes and complex backgrounds of drone images usually result in weak feature representation of small objects, which makes it challenging to achieve high-precision object detection. Aiming to improve small objects detection, this paper proposes a novel cross-scale knowledge distillation (CSKD) method, which enhances the features of small objects in a manner similar to image enlargement, so it is termed as ZoomInNet. First, based on an efficient feature pyramid network structure, the teacher and student network are trained with images in different scales to introduce the cross-scale feature. Then, the proposed layer adaption (LA) and feature level alignment (FA) mechanisms are applied to align the feature size of the two models. After that, the adaptive key distillation point (AKDP) algorithm is used to get the crucial positions in feature maps that need knowledge distillation. Finally, the position-aware L2 loss is used to measure the difference between feature maps from cross-scale models, realizing the cross-scale information compression in a single model. Experiments on the challenging Visdrone2018 dataset show that the proposed method draws on the advantages of the image pyramid methods, while avoids the large calculation of them and significantly improves the detection accuracy of small objects. Simultaneously, the comparison with mainstream methods proves that our method has the best performance in small object detection.

Fast and accurate detection of small, dense, and background-complex objects in UAV aerial images has become a key challenge in the field of object detection. To address this issue, this paper proposes an improved model named JKAN-YOLO based on the YOLOv10s detection framework. The proposed method replaces part of the convolutional structures in YOLOv10s with JacobiKANConv modules to enhance the model's sensitivity to edge details and small-scale targets, while effectively reducing computational resource consumption. Experimental results on the VisDrone2019 dataset show that JKAN-YOLO improves mAP@0.5, Precision, and Recall by 4.60%, 2.82%, and 2.61%, respectively, with mAP@0.5 reaching 41.0%, and FLOPs reduced from 24.5 G to 22.1 G. On DOTA-v1. 0 and Tiny Person datasets, the model also demonstrates strong generalization ability, achieving mAP@0.5 scores of 40.5% and 25.1%, respectively. The experimental results indicate that JKAN-YOLO significantly improves the detection accuracy and stability of small objects while maintaining detection speed, showing promising application potential in real-world scenarios.

Multi-granularity detector for enhanced small object detection under sample imbalance

<p indent=0mm>Existing object detection algorithms only use a fixed size convolution kernel when extracting features, ignoring the difference in the receptive field of different scale features, which affects the detection effect of different scale objects. To solve this problem, a multi-scale object detection network based on multi-branch parallel dilated convolution is proposed. Firstly, the basic network VGG-16 is used to extract the features of the image. Secondly, a multi-branch parallel dilated convolution is designed to extract multi-scale features to improve object detection ability of the network. Then, a non-local block is employed to integrate the global spatial information and enhance the context information. Finally, the object detection and location tasks are performed on feature maps with different scales. Experimental results on PASCAL VOC and MS COCO datasets demonstrate that the proposed method can effectively improve the detection accuracy of different scale objects and clearly improve the detection accuracy of small objects.

Abstract: Satellite image analysis plays a crucial role in fields such as environmental monitoring, urban planning, agriculture, and disaster management. This project presents a machine learning-based methodology for the automated segmentation and classification of objects in satellite images. The proposed system employs a two-step algorithm: (1) K-Means clustering for unsupervised segmentation of satellite images into meaningful clusters, and (2) Support Vector Machine (SVM) for supervised classification of the segmented regions into predefined categories such as land, water, and vegetation. A Flask-based web application is developed to provide a userfriendly interface for uploading satellite images and visualizing classified results. The backend is supported by a structured SQL database to manage image metadata and classification outputs efficiently. The system is designed to handle large datasets, ensuring robustness and scalability. Experimental results on benchmark datasets demonstrate high accuracy in object detection and classification, highlighting the system's potential to support real-world decision-making in environmental and infrastructural domains.

The objective of this study is to examine two powerful components: the YOLO (You Only Look Once) framework for object recognition on edge devices and the MobileNet architecture. With the rising demand for real-time, efficient, and accurate object detection in constrained environments, these models offer distinct advantages suited to different applications. YOLO is renowned for its high accuracy and speed, making it ideal for complex detection tasks, whereas MobileNet is optimized for efficiency, enabling deployment on devices with limited computational resources. The paper discusses the evolution of these models, their architectural differences, and their respective strengths and limitations when implemented on edge devices. Furthermore, we explore the feasibility of integrating these frameworks into a hybrid model that leverages the lightweight feature extraction of MobileNet and the robust detection capabilities of YOLO. Such a hybrid approach could significantly enhance real-time object detection performance across a wide range of hardware platforms.&nbsp; Researchers and practitioners seeking to deploy advanced object detection models in real-world applications, particularly where computational efficiency and accuracy are paramount may find this review as a valuable resource.