Advancements in computer vision research have put transformer architecture as the state-of-the-art in computer vision tasks. One of the known drawbacks of the transformer architecture is the high number of parameters, this can lead to a more complex and inefficient algorithm. This paper aims to reduce the number of parameters and in turn, made the transformer more efficient. We present Sparse Transformer (SparTa) Block, a modified transformer block with an addition of a sparse token converter that reduces the dimension of high-level features to the number of latent tokens. We implemented the SparTa Block within the Swin-T architecture (SparseSwin) to leverage Swin's proficiency in extracting low-level features and enhance its capability to extract information from high-level features while reducing the number of parameters. The proposed SparseSwin model outperforms other state-of-the-art models in image classification with an accuracy of 87.26%, 97.43%, and 85.35% on the ImageNet100, CIFAR10, and CIFAR100 datasets respectively. Despite its fewer parameters, the result highlights the potential of a transformer architecture using a sparse token converter with a limited number of tokens to optimize the use of the transformer and improve its performance. The code is available at https://github.com/KrisnaPinasthika/SparseSwin. 

SparseSwin: Swin transformer with sparse transformer block

Input optimization is a distinguishing characteristic of Precision Agriculture approaches, helping reduce the environmental impact and costs and increase vegetable production quality. Thanks to the high automation evolution of Unmanned Aerial Systems (UAS), a new approach derived from their combination with Deep Learning techniques is leading to significant improvements in agricultural management practices. The study aims at artichoke plants detection and georeferencing as a first step for an on-the-fly, real time, UAS spraying system, and use the gathered information to monitor crop development through a multi-temporal approach. A commercial UAS, equipped with an RGB sensor, acquired images of the artichoke field located in Sardinia (Italy) during the 2021–2022 season in different crop growth stages. The FPN (Feature Pyramid Network), trained and compared with the YOLOv5 (You Only Look Once) network, showed a high detection level with an average F1 score of around 90%, and satisfactory off-line performances on the Nvidia Jetson Nano board. YOLOv5 achieved the best overall result. The FPN recorded a lower recall, which is desirable to achieve a minimum number of detection errors and limit the leakage of agrochemicals on false-positive targets. The multi-temporal approach influenced detection performances, with an inverse response of precision and recall metrics. The growing index trend showed a distinct value in October, peaking at the beginning of December as expected. The proposed approach contributes to designing future automatic and reliable site-specific UAS agrochemicals application and the classification of management zones. 

Artichoke deep learning detection network for site-specific agrochemicals UAS spraying

To create a clean living environment, governments around the world have hired a large number of workers to clean up waste on pavements, which is inefficient for waste management. To better alleviate this problem, relevant scholars have proposed several deep learning methods based on RGB images to achieve waste detection and recognition. Considering the limitations of color images, we propose an efficient multi-modal learning solution for pavement waste detection and recognition. Specifically, we construct a high-quality outdoor pavement waste dataset called OPWaste, which is more in line with real needs. Compared to other waste datasets, OPWaste dataset not only has the advantages of rich background and high diversity, but also provides color and depth images. Meanwhile, we explore six different multi-modal fusion methods and propose a novel multi-modal multi-scale network (MM-Net) for RGB-D waste detection and recognition. MM-Net introduces a novel multi-scale refinement module (MRM) and multi-scale interaction module (MIM). MRM can effectively refine critical features using attention mechanisms. MIM can gradually realize information interaction between hierarchical features. In addition, we select several representative methods and perform comparative experiments. Experimental results show that MM-Net based on the image addition fusion method outperforms other deep learning models and reaches 97.3% and 84.4% on mAP0.5 and AR metrics. In fact, multi-modal learning plays an important role in intelligent waste recycling. As a promising auxiliary tool, our solution can be applied to intelligent cleaning robots for automatic outdoor waste management. 

Multi-modal deep learning networks for RGB-D pavement waste detection and recognition.

Small object detection in diverse application landscapes: a survey

Waste detection is one of the main problems preventing the realization of automated waste classification, which is a basic function for robotic arms. In addition to object identification in general image analysis, a waste-sorting robotic arm not only needs to identify a target object but also needs to accurately judge its placement angle so that it can determine an appropriate angle for grasping. In order to solve the problem of low-accuracy image detection caused by irregular placement angles, in this work, we propose an improved oriented waste detection method based on YOLOv5. By optimizing the detection head of the YOLOv5 model, this method can generate an oriented detection box for a waste object that is placed at any angle. Based on the proposed scheme, we further improved three aspects of the performance of YOLOv5 in the detection of waste objects: the angular loss function was derived based on dynamic smoothing to enhance the model’s angular prediction ability, the backbone network was optimized with enhanced shallow features and attention mechanisms, and the feature aggregation network was improved to enhance the effects of feature multi-scale fusion. The experimental results showed that the detection performance of the proposed method for waste targets was better than other deep learning methods. Its average accuracy and recall were 93.9% and 94.8%, respectively, which were 11.6% and 7.6% higher than those of the original network, respectively.

/pdf/improved-method-for-oriented-waste-detection-2vmpcap9.pdf

Improved Method for Oriented Waste Detection

Over the past few years, as environmental problems have been progressively deteriorating, waste classification has also become a research hotspot. As an algorithm with better detection accuracy and speed, SSD (Single Shot MultiBox Detector) has made great progress in many aspects. However, it can’t achieve a good detection effect for small objects because it does not make full use of high-level semantic information. In this paper, we propose a MobileNet-SSD model with FPN to solve the problem of waste detection, which can reduce parameters, narrow internal space and improve performance for small objects compared with SSD model. Besides, Focal Loss is adopted to reduce the imbalance between foreground and background samples to enhance detector effect. To verify the effectiveness of the approach proposed here, a dataset for waste detection is collected and annotated. As revealed from the experimental results, the mAP of our method is 93.63% and the speed is 102 FPS, which can beat other methods.

A MobileNet-SSD Model with FPN for Waste Detection

In this paper, we derive the necessary and sufficient conditions of surface pencil pair interpolating Bertrand pair as common asymptotic curves in Galilean space G3. Moreover, the conclusion to rul...

Erratum: Surface pencil pair interpolating Bertrand pair as common asymptotic curves in Galilean space G3

Improving Accuracy of Pneumonia Classification Using Modified DenseNet.

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A MobileNet-SSD Model with FPN for Waste Detection

Erratum: Surface pencil pair interpolating Bertrand pair as common asymptotic curves in Galilean space G3

Improving Accuracy of Pneumonia Classification Using Modified DenseNet.