Lightweight CNN model: automated vehicle detection in aerial images

Momin, Md. Abdul; Junos, Mohamad Haniff; Khairuddin, Anis Salwa Mohd; Talip, Mohamad Sofıan Abu

doi:10.1007/s11760-022-02328-7

Cited by 11 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improve the RSS algorithm with spatiotemporal information technology to suppress the impact of complex backgrounds on object detection and reduce false detections. Momin M A et al [23] proposed a lightweight algorithm model that is feasible with limited computing resources. This algorithm model is based on YOLOv4-Tiny by using three prediction boxes and adding the second layer and the third layer to the backbone network.…”

Section: Introductionmentioning

confidence: 99%

Drone-TOOD: A Lightweight Task-Aligned Object Detection Algorithm for Vehicle Detection in UAV Images

Ou,

Dong,

Liu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Vehicle object detection using UAV images is a crucial undertaking in urban traffic management and the advancement of autonomous driving technologies. Conventional networks fail to achieve accuary in detecting vehicle objects from a drone's perspective due to the significant variations in the size of the target items, unequal distribution of their positions in the image, and image degradation induced by the drone's movement.In order to surmount this challenge, this research suggests an enhanced TOOD object detection model named Drone-TOOD. The first proposal is to create a lightweight network skeleton called CSPRegNet by merging CSPblock with Regblock. Secondly, we incorporate Regblock into CSPPAFPN to enhance CSPRegPAFPN and incorporate EVCblock at the upsampling location of deep features to capture corner area details and minimize the degradation of feature information. In addition, a efficient task decomposition attention module is also proposed to enhance the interaction ability of positioning and classification tasks. This task decomposition module can highlight the characteristics of a specific task while retaining the characteristics of another task, thereby improving detection capabilities. Experiments conducted on the Drone Vision Challenge Benchmark (VisDrone) demonstrate that the enhanced model can obtain superior performance compared to TOOD. The average precision (mAP) achieved by our approach is 64%, surpassing TOOD by 7.9%. The frames per second (FPS) stayed constant at 27.2. Drone-TOOD demonstrates superior performance compared to other lightweight models on the VisDrone-2021 dataset. In order to demonstrate the robustness of our approach, we additionally performed ablation experiments and conducted tests on the UAV Detection and Tracking Dataset (UAVDT), resulting in an achieved mean average precision (mAP) of 64.6%. Furthermore, Drone-TOOD possesses a total of parameter approximately 21.7 M.

show abstract

Section: Introductionmentioning

confidence: 99%

Drone-TOOD: A Lightweight Task-Aligned Object Detection Algorithm for Vehicle Detection in UAV Images

Ou,

Dong,

Liu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Network models based on DL approaches can map complex nonlinear relationships and extract richer features. Two categories of target detection network models are continually formed and optimized due to the development of hardware technology and enormous data: single-stage networks (i.e., SSD and YOLOv3) and two-stage networks (i.e., cascade RCNN and fast RCNN) [10].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

Alajmi,

Alamro,

Al-Mutiri

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Remote sensing imagery involves capturing and examining details about the Earth’s surface from a distance, often using satellites, drones, or other aerial platforms. It offers useful data with which to monitor and understand different phenomena on Earth. Vehicle detection and classification play a crucial role in various applications, including traffic monitoring, urban planning, and environmental analysis. Deep learning, specifically convolutional neural networks (CNNs), has revolutionized vehicle detection in remote sensing. This study designs an improved Chimp optimization algorithm with a DL-based vehicle detection and classification (ICOA-DLVDC) technique on RSI. The presented ICOA-DLVDC technique involves two phases: object detection and classification. For vehicle detection, the ICOA-DLVDC technique applies the EfficientDet model. Next, the detected objects can be classified by using the sparse autoencoder (SAE) model. To optimize the SAE’s hyperparameters effectively, we introduce an ICOA which streamlines the parameter tuning process, accelerating convergence and enhancing the overall performance of the SAE classifier. An extensive set of experiments has been conducted to highlight the improved vehicle classification outcomes of the ICOA-DLVDC technique. The simulation values demonstrated the remarkable performance of the ICOA-DLVDC approach compared to other recent techniques, with a maximum accuracy of 99.70% and 99.50% on the VEDAI dataset and ISPRS Postdam dataset, respectively.

show abstract

“…CNN excels at image-based tasks due to its advantages in learning the spatial structure of the pixels automatically [26], which produces multiple DL network architectures, such as Unet [27], Unet++ [28], multi-scale attention network (MAnet) [29], and pyramid scene parsing network (PSPnet) [30]. In addition, these CNNs have been widely applied in medical image segmentation [28] and the target detection of vehicles [31], crops [13], and trees [32], which received an outstanding performance.…”

Section: Introductionmentioning

confidence: 99%

Rubber Tree Recognition Based on UAV RGB Multi-Angle Imagery and Deep Learning

Liang,

Sun,

Kou

et al. 2023

Drones

View full text Add to dashboard Cite

The rubber tree (Hevea brasiliensis) is an important tree species for the production of natural latex, which is an essential raw material for varieties of industrial and non-industrial products. Rapid and accurate identification of the number of rubber trees not only plays an important role in predicting biomass and yield but also is beneficial to estimating carbon sinks and promoting the sustainable development of rubber plantations. However, the existing recognition methods based on canopy characteristic segmentation are not suitable for detecting individual rubber trees due to their high canopy coverage and similar crown structure. Fortunately, rubber trees have a defoliation period of about 40 days, which makes their trunks clearly visible in high-resolution RGB images. Therefore, this study employed an unmanned aerial vehicle (UAV) equipped with an RGB camera to acquire high-resolution images of rubber plantations from three observation angles (−90°, −60°, 45°) and two flight directions (SN: perpendicular to the rubber planting row, and WE: parallel to rubber planting rows) during the deciduous period. Four convolutional neural networks (multi-scale attention network, MAnet; Unet++; Unet; pyramid scene parsing network, PSPnet) were utilized to explore observation angles and directions beneficial for rubber tree trunk identification and counting. The results indicate that Unet++ achieved the best recognition accuracy (precision = 0.979, recall = 0.919, F-measure = 94.7%) with an observation angle of −60° and flight mode of SN among the four deep learning algorithms. This research provides a new idea for tree trunk identification by multi-angle observation of forests in specific phenological periods.

show abstract

Lightweight CNN model: automated vehicle detection in aerial images

Cited by 11 publications

References 28 publications

Drone-TOOD: A Lightweight Task-Aligned Object Detection Algorithm for Vehicle Detection in UAV Images

Drone-TOOD: A Lightweight Task-Aligned Object Detection Algorithm for Vehicle Detection in UAV Images

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

Rubber Tree Recognition Based on UAV RGB Multi-Angle Imagery and Deep Learning

Contact Info

Product

Resources

About