The object detection algorithm is mainly focused on detection in general scenarios, when the same algorithm is applied to drone-captured scenes, and the detection performance of the algorithm will be significantly reduced. Our research found that small objects are the main reason for this phenomenon. In order to verify this finding, we choose the yolov5 model and propose four methods to improve the detection precision of small object based on it. At the same time, considering that the model needs to be small in size, speed fast, low cost and easy to deploy in actual application, therefore, when designing these four methods, we also fully consider the impact of these methods on the detection speed. The model integrating all the improved methods not only greatly improves the detection precision, but also effectively reduces the loss of detection speed. Finally, based on VisDrone-2020, the mAP of our model is increased from 12.7 to 37.66%, and the detection speed is up to 55FPS. It is to outperform the earlier state of the art in detection speed and promote the progress of object detection algorithms on drone platforms.

An improved Yolov5 real-time detection method for small objects captured by UAV

The aim of this research is to show the implementation of object detection on drone videos using TensorFlow object detection API. The function of the research is the recognition effect and performance of the popular target detection algorithm and feature extractor for recognizing people, trees, cars, and buildings from real-world video frames taken by drones. The study found that using different target detection algorithms on the “normal” image (an ordinary camera) has different performance effects on the number of instances, detection accuracy, and performance consumption of the target and the application of the algorithm to the image data acquired by the drone is different. Object detection is a key part of the realization of any robot’s complete autonomy, while unmanned aerial vehicles (UAVs) are a very active area of this field. In order to explore the performance of the most advanced target detection algorithm in the image data captured by UAV, we have done a lot of experiments to solve our functional problems and compared two different types of representative of the most advanced convolution target detection systems, such as SSD and Faster R-CNN, with MobileNet, GoogleNet/Inception, and ResNet50 base feature extractors.

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Object Detection from the Video Taken by Drone via Convolutional Neural Networks

A method for automatic real-time detection and counting of fruit fly pests in orchards by trap bottles via convolutional neural network with attention mechanism added

Pork primal cuts recognition method via computer vision.

Statistical analysis of Bactrocera grooming behavior is important for pest control and human health. Based on DeepLabCut, this study proposes a noninvasive and effective method to track the key points of Bactrocera minax and to detect and analyze its grooming behavior. The results are analyzed and calculated automatically by a computer program. Traditional movement tracking methods are invasive; for instance, the use of artificial pheromone may affect the behavior of Bactrocera minax, thus directly affecting the accuracy and reliability of experimental results. Traditional research studies mainly rely on manual work for behavior analysis and statistics. Researchers need to play the video frame by frame and record the time interval of each grooming behavior manually, which is time-consuming, laborious, and inaccurate. So the advantages of automated analysis are obvious. Using the method proposed in this paper, the image data of 94538 frames from 5 adult Bactrocera were analyzed and 14 key points were tracked. The overall tracking accuracy was as high as 96.7%. In the behavior analysis and statistics, the average accuracy rate of the five grooming behavior was all above 96%, and the accuracy rate of the remaining two grooming behavior was over 87%. The experimental results show that the automatic noninvasive method designed in this paper can track many key points of Bactrocera minax with high accuracy and ensure the accuracy of insect behavior recognition and analysis, which greatly reduces the manual observation time and provides a new method for key points tracking and behavior recognition of related insects.

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Key Points Tracking and Grooming Behavior Recognition of Bactrocera minax (Diptera: Trypetidae) via DeepLabCut

Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Zhangzhang He

Papers

Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

A method for automatic real-time detection and counting of fruit fly pests in orchards by trap bottles via convolutional neural network with attention mechanism added

Key Points Tracking and Grooming Behavior Recognition of Bactrocera minax (Diptera: Trypetidae) via DeepLabCut