ISA: Ingenious Siamese Attention for object detection algorithms towards complex scenes

Compared with the traditional object detection algorithm, the object detection algorithm based on deep learning has stronger robustness to complex scenarios, which is the hot direction of current research. According to the process characteristics of the object detection algorithm based on deep learning, it is divided into two-stage object detection algorithm and single-stage object detection algorithm, focusing on the problems solved by some classical algorithms and their advantages and disadvantages. In view of the problem of object detection, especially small object detection, the commonly used data sets and performance evaluation indicators are summarized; the characteristics, advantages, and detection difficulties of various common data sets are compared; the challenges faced by commonly used object detection methods and small object detection are systematically summarized; the latest work of small object detection methods based on deep learning is sorted out; and the small object detection methods based on multiscale and small object detection methods based on super-resolution are introduced. At the same time, the lightweight strategy for target detection methods and the performance of some lightweight models are introduced; the characteristics, advantages, and limitations of various methods are summarized; and the future development direction of small object detection methods based on deep learning is prospected.

Air-to-ground object detection has important application value in many fields such as military reconnaissance, air strike, personnel rescue, and natural exploration. Object detection algorithm based on deep learning solves the problems of poor generality and low robustness of traditional manual detection algorithms. It has great advantages in air-to-ground detection tasks, but for small unmanned platforms, currently deep learning object detection algorithms have high hardware requirements and are difficult to deploy. In this paper, a deep learning object detection simulation system is built based on the TX2 embedded development board with the features of low power consumption, light weight and fast calculation speed. This object detection simulation experiment is performed for two mainstream deep learning object detection algorithms. The experimental results showed that the regression-based SSD algorithm combined with MobileNets feature extraction network could perform real-time in the embedded system, which laid the foundation for the actual construction of the next unmanned aerial platform.

In the era of the Internet of Things, the construction of edge computing platform has become more and more important, which has led lots of object detection applications being deployed on embedded devices. However, traditional object detection algorithms require lots of engery and a large amount of well labeled samples for training. The time spent on model training and data labeling also slows down the upgrade iteration of applications. Therefore, an object detection algorithm that requires only few energy and a few samples to update parameters could help the long-term benign development of IoT technology. In this paper, we propose an effective object detection method based on the few-shot learning, which could achieve considerable performance with few data for novel(new) classes. Our well-designed strategies could alleviate the impact of scale variation in support set under few-shot setting. Through extensive experiments, we prove that our model is superior to well-recognized baselines on few-shot object detection task.

Detecting objects in intricate scenes has always presented a significant challenge in the field of machine vision. Complex scenes typically refer to situations in images or videos where there are numerous densely distributed and mutually occluded objects, making the object detection task even more difficult. This paper introduces a novel head detection algorithm, YOLO-Based Head Detection in Complex Scenes (YOLO-HDCS). Firstly, in complex scenes, head detection typically involves a large number of small objects that are randomly distributed. Traditional object detection algorithms struggle to address the challenge of small object detection. For this purpose, two new modules have been constructed: one is a feature fusion module based on context enhancement with scale adjustment, and the other is an attention-based convolutional module. These modules are characterized by high detection efficiency and high accuracy. They significantly improve the model's multi-scale detection capabilities, thus enhancing the detection ability of the system. Secondly, it was found in practical operations that the original IoU function has a serious problem with overlapping detection in complex scenes. There is an IoU function that can ensure that the final selection boxes cover the object as accurately as possible without overlapping. This not only improves the detection performance but also greatly aids in enhancing the detection efficiency and accuracy. Our method achieves impressive results for head detection in complex scenarios, with average accuracy of 82.2%, and has the advantage of rapid loss convergence during training.

In recent years, computer vision technology has developed rapidly, as one of its important research directions, object detection has received widespread attention due to its high accuracy. Meanwhile, object detection has many application fields, such as intelligent transportation, medical and health, security systems, etc. Traditional object detection methods have limitations when applied to complex real-world scenarios. To improve the shortcomings of conventional methods, deep learning based object detection algorithms have significantly improved the efficiency of object detection and become a research hotspot in object detection. This article summarizes the algorithms into two-stage and one-stage object detection algorithms based on the technical processes and structural differences in handling object detection tasks. Firstly, several common two-stage and one-stage object detection algorithms and their applications in real-world scenarios are introduced. Then their data sets and algorithm performance are analyzed and compared. Finally, according to the results of the comparison, the existing problems of the two-stage object detection algorithm and the one-stage object detection method are discussed, and their future development directions are pointed out.

The paper presents review of key aspects associated with object detection and tracking algorithms in sensing systems for automotive applications covering active safety, advanced driver assistance and autonomous driving systems. The role of discussed systems is to establish location and monitor relative movement of objects and environment with main focus on identification of vulnerable road users. The object detection algorithms allow to classify and differentiate both static and dynamic objects in the vehicle’s surrounding. The analysis will be focused on selection of most suitable platform for development of optimized and safe vulnerable road user detection system.

Many fields are currently investigating the use of convolutional neural networks to detect specific objects in three-dimensional data. While algorithms based on three-dimensional data are more stable and insensitive to lighting conditions than algorithms based on two-dimensional image data, they require more computation than two-dimensional data, making it difficult to drive CNN algorithms using three-dimensional data in lightweight embedded systems. In this paper, we propose a method to process three-dimensional data through a simple algorithm instead of complex operations such as convolution in CNN, and utilize its physical characteristics to generate ROIs to perform a CNN object detection algorithm based on two-dimensional image data. After preprocessing the LiDAR point cloud data, it is separated into individual objects through clustering, and semantic detection is performed through a classifier trained based on machine learning by extracting physical characteristics that can be utilized for semantic detection. The final object recognition is performed through a 2D-based object detection algorithm that bypasses the process of tracking bounding boxes by generating individual 2D image regions from the location and size of objects initially detected by semantic detection. This allows us to utilize the physical characteristics of 3D data to improve the accuracy of 2D image-based object detection algorithms, even in environments where it is difficult to collect data from camera sensors, resulting in a lighter system than 3D data-based object detection algorithms. The proposed model achieved an accuracy of 81.84% on the YOLO v5 algorithm on an embedded board, which is 1.92% higher than the typical model. The proposed model achieves 47.41% accuracy in an environment with 40% higher brightness and 54.12% accuracy in an environment with 40% lower brightness, which is 8.97% and 13.58% higher than the general model, respectively, and can achieve high accuracy even in non-optimal brightness environments. The proposed technique also has the advantage of reducing the execution time depending on the operating environment of the detection model.

Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB building blocks into the backbone network, along with the optimization of the C2f structure and the incorporation of large-kernel depthwise convolutions, effectively enhances the model’s receptive field. This improvement increases the capability of detecting multi-scale objects in complex underwater environments without adding a computational burden. Next, the incorporation of a Partial Self-Attention (PSA) module at the end of the backbone network enhances model efficiency and optimizes the utilization of computational resources while maintaining high performance. Finally, the integration of the Neck component from the Gold-YOLO model improves the neck structure of the YOLOv8 model, facilitating the fusion and distribution of information across different levels, thereby achieving more efficient information integration and interaction. Experimental results show that YOLOv8-CPG significantly outperforms the traditional YOLOv8 in underwater environments. Precision and Recall show improvements of 2.76% and 2.06%. Additionally, mAP50 and mAP50-95 metrics have increased by 1.05% and 3.55%, respectively. Our approach provides an efficient solution to the difficulties encountered in underwater object detection.

Object detection and tracking has always been one of the important research directions in computer vision. The purpose is to determine whether the object is contained in the input image and enclose the object with a bounding box. However, most object detection and tracking methods are applied to daytime objects, and the processing of nighttime objects is imprecise. In this paper, a spectral-spatial feature enhancement algorithm for nighttime object detection and tracking is proposed, which is inspired by symmetrical neural networks. The proposed method consists of the following steps. First, preprocessing is performed on unlabeled nighttime images, including low-light enhancement, object detection, and dynamic programming. Second, object features for daytime and nighttime times are extracted and modulated with a domain-adaptive structure. Third, the Siamese network can make full use of daytime and nighttime object features, which is trained as a tracker by the above images. Fourth, the test set is subjected to feature enhancement and then input to the tracker to obtain the final detection and tracking results. The feature enhancement step includes low-light enhancement and Gabor filtering. The spatial-spectral features of the target are fully extracted in this step. The NAT2021 dataset is used in the experiments. Six methods are employed as comparisons. Multiple judgment indicators were used to analyze the research results. The experimental results show that the method achieves excellent detection and tracking performance.

In practice, the object detection algorithm is limited by a complex detection environment, hardware costs, computing power, and chip running memory. The performance of the detector will be greatly reduced during operation. Determining how to realize real-time, fast, and high-precision pedestrian recognition in a foggy traffic environment is a very challenging problem. To solve this problem, the dark channel de-fogging algorithm is added to the basis of the YOLOv7 algorithm, which effectively improves the de-fogging efficiency of the dark channel through the methods of down-sampling and up-sampling. In order to further improve the accuracy of the YOLOv7 object detection algorithm, the ECA module and a detection head are added to the network to improve object classification and regression. Moreover, an 864 × 864 network input size is used for model training to improve the accuracy of the object detection algorithm for pedestrian recognition. Then the combined pruning strategy was used to improve the optimized YOLOv7 detection model, and finally, the optimization algorithm YOLO-GW was obtained. Compared with YOLOv7 object detection, YOLO-GW increased Frames Per Second (FPS) by 63.08%, mean Average Precision (mAP) increased by 9.06%, parameters decreased by 97.66%, and volume decreased by 96.36%. Smaller training parameters and model space make it possible for the YOLO-GW target detection algorithm to be deployed on the chip. Through analysis and comparison of experimental data, it is concluded that YOLO-GW is more suitable for pedestrian detection in a fog environment than YOLOv7.

Poor weather circumstances including fog, dust, and atmospheric dispersion of other particles can degrade the clarity of photos taken in outdoor settings. High-level computer vision tasks like picture segmentation and object detection may become more difficult as a result of this issue. However, earlier research on image dehazing, such as Dark Channel Prior (DCP), suffers from a significant computing burden and degradation of the original image, such as oversaturation and halos. In this work, an improved image dehazing approach (Fast DCP) based on down sampling has been proposed. The proposed algorithm executes the dehazing of a haze image with a smaller amount computational time without degrading the quality of the image. The performance measures of proposed algorithm have been analysed in terms of PSNR, MMSE and SSIM and compared with existing DCP algorithm. The simulation results proven that, the computational time for proposed algorithm has been reduced by 70% when compared to DCP without degrading image quality.

Images captured in poor lighting conditions (such haze, fog, mist, or smog) have a lower level of visibility because air particles deflect light. Single picture dehazing techniques can restore clarity to a single hazy image. Even though Dark channel prior (DCP) has been the most used method for image dehazing algorithms, it has poor picture quality and requires a lot of adjustments for real time applications such as; computer vision, object detection. However, previous studies such as; Dark Channel Prior (DCP) on image dehazing agonises from a gigantic processing time and sleaze of the original image. In addition, the dimensions of the images have a significant effect on the performance of the dehazing algorithms. Hence, the main motive of this work is to minimize the computational overload either by reducing the size of the image or by accurate transmission map estimation without compromising the image quality. Hence, this work is focused on reducing the computational time for image dehazing through multilevel discrete (Haar) wavelet transform, in which the image dimensions has been reduced without degrading quality. The performance measures of proposed algorithm have been analysed in terms of PSNR, MMSE and SSIM and compared with existing DCP algorithm. The simulation results proven that, the computational time for proposed algorithm has been reduced by 90% when compared to DCP without degrading image quality.

Automation is spread in all daily life and business activities to facilitate human life and working conditions. Robots, automated cars, unmanned vehicles, robot arms, automated factories etc. are getting place in our lives. For these automated actors, one important task is recognizing objects and obstacles in the target environment. Object detection, determining the objects and their location in the environment, is one of the most important solution for this task. With deep learning techniques like Convolutional Neural Network and GPU processing, object detection has become more accurate and faster, and getting attention of researchers. In recent years, many articles about object detection algorithms and usage of object detection have been published. There are surveys about the object detection algorithms, but they have introduced algorithms and focused on common application areas. With this survey, we aim to show that object detection algorithms have very large and different application area. In this study, we have given a brief introduction to deep learning. We have then focused on standard object detection algorithms based on deep learning and their applications in different research areas in recent years to give an idea for future works. Also, the datasets and evaluation metrics used in the research are listed.

Low-light object detection involves identifying and locating objects in images captured under poor lighting conditions. It plays a significant role in surveillance and security, night pedestrian recognition, and autonomous driving, showcasing broad application prospects. Most existing object detection algorithms and datasets are designed for normal lighting conditions, leading to a significant drop in detection performance when applied to low-light environments. To address this issue, we propose a Low-Light Detection with Low-Light Enhancement (LDWLE) framework. LDWLE is an encoder-decoder architecture where the encoder transforms the raw input data into a compact, abstract representation (encoding), and the decoder gradually generates the target output format from the representation produced by the encoder. Specifically, during training, low-light images are input into the encoder, which produces feature representations that are decoded by two separate decoders: an object detection decoder and a low-light image enhancement decoder. Both decoders share the same encoder and are trained jointly. Throughout the training process, the two decoders optimize each other, guiding the low-light image enhancement towards improvements that benefit object detection. If the input image is normally lit, it first passes through a low-light image conversion module to be transformed into a low-light image before being fed into the encoder. If the input image is already a low-light image, it is directly input into the encoder. During the testing phase, the model can be evaluated in the same way as a standard object detection algorithm. Compared to existing object detection algorithms, LDWLE can train a low-light robust object detection model using standard, normally lit object detection datasets. Additionally, LDWLE is a versatile training framework that can be implemented on most one-stage object detection algorithms. These algorithms typically consist of three components: the backbone, neck, and head. In this framework, the backbone functions as the encoder, while the neck and head form the object detection decoder. Extensive experiments on the COCO, VOC, and ExDark datasets have demonstrated the effectiveness of LDWLE in low-light object detection. In quantitative measurements, it achieves an AP of 25.5 and 38.4 on the synthetic datasets COCO-d and VOC-d, respectively, and achieves the best AP of 30.5 on the real-world dataset ExDark. In qualitative measurements, LDWLE can accurately detect most objects on both public real-world low-light datasets and self-collected ones, demonstrating strong adaptability to varying lighting conditions and multi-scale objects.

Object detection is an important part of remote sensing image analysis. With the development of the earth observation technology and convolutional neural network, remote sensing image object detection technology based on deep learning has received more and more attention and research. At present, many excellent object detection algorithms have been proposed and applied in the field of remote sensing. In this paper, the object detection algorithms of remote sensing image is systematically summarized, the main contents include the traditional remote sensing image object detection method and the method based on deep learning, emphasis on summarize the remote sensing image object detection algorithm based on deep learning and its development course, then we introduced the rule of performance evaluation of object detection and datasets that commonly used. Finally, the future development trend is analyzed and prospected. It is hoped that this summary and analysis can provide some reference for future research on object detection technology in remote sensing field.