Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3′s head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection.

With the continuous development of artificial intelligence, embedding object detection algorithms into autonomous underwater detectors for marine garbage cleanup has become an emerging application area. Considering the complexity of the marine environment and the low resolution of the images taken by underwater detectors, this paper proposes an improved algorithm based on Mask R-CNN, with the aim of achieving high accuracy marine garbage detection and instance segmentation. First, the idea of dilated convolution is introduced in the Feature Pyramid Network to enhance feature extraction ability for small objects. Secondly, the spatial-channel attention mechanism is used to make features learn adaptively. It can effectively focus attention on detection objects. Third, the re-scoring branch is added to improve the accuracy of instance segmentation by scoring the predicted masks based on the method of Generalized Intersection over Union. Finally, we train the proposed algorithm in this paper on the Transcan dataset, evaluating its effectiveness by various metrics and comparing it with existing algorithms. The experimental results show that compared to the baseline provided by the Transcan dataset, the algorithm in this paper improves the mAP indexes on the two tasks of garbage detection and instance segmentation by 9.6 and 5.0, respectively, which significantly improves the algorithm performance. Thus, it can be better applied in the marine environment and achieve high precision object detection and instance segmentation.

Environmental protection is still a key issue that cannot be ignored at this stage of social development. With the development of artificial intelligence, various technologies increasingly tend to be widely used in the field of environmental protection, such as searching the wilderness through an unmanned aerial vehicle (UAV) and cleaning garbage by robots. Traditional object detection algorithms for this scenario suffer from low accuracy and high computational cost. Therefore, this paper proposes an algorithm applied to automatic garbage detection and instance segmentation in complex scenes. First, we construct sample-fused feature pyramid networks (SF-FPN) to achieve multi-scale feature sampling on multiple levels, to enhance the semantic representation of features. Second, adding the mask branch based on conditional convolution, introducing the idea of instance-filters to automatically generate the filter parameters of the Fully Convolutional Networks (FCN), to realize the instance-level pixel classification. Moreover, the Atrous Spatial Pyramid Pooling (ASPP) module is introduced to encode the feature information in a dense way to assist the generation of MASK. Finally, the object is detected and the instance is segmented by a two-branch structure. In addition, we also perform data augmentation on the original dataset to prevent model overfitting. The proposed algorithm reaches 82.7 and 72.4 according to the mAP index of detection and instance segmentation while using the public TACO dataset.