SCD: A Stacked Carton Dataset for Detection and Segmentation.

Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons and the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this article, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images were collected from the Internet and several warehouses, and objects were labeled for precise localization using instance mask annotation. There were a total of 250,000 instance masks from 16,136 images. Naturally, a suite of benchmarks was established with several popular detectors and instance segmentation models. In addition, we designed a carton detector based on RetinaNet by embedding our proposed Offset Prediction between the Classification and Localization module (OPCL) and the Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality, which boosts AP by 3.1∼4.7% on SCD at the model level, while BGS guides the detector to pay more attention to the boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL for other datasets, we conducted extensive experiments on MS COCO and PASCAL VOC. The improvements in AP on MS COCO and PASCAL VOC were 1.8∼2.2% and 3.4∼4.3%, respectively.

The interspecific competition of tree plants in the presence of AM fungi and litter facilitates root morphological development and nutrition when compared with intraspecific competition.

Arbuscular mycorrhizal (AM) fungi can affect plant growth by regulating competition. Nutrient-deficient karst habitats contain abundant plants that compete for nutrients through interspecific or intraspecific competition, involving the nutritional transformation of litter decomposition. However, how plant competition in the presence of AM fungi and litter affects root development and nutrition remains unclear. A potted experiment was conducted, including AM fungus treatment with or without Glomus etunicatum, the competition treatment concerning intraspecific or interspecific competition through planting Broussonetia papyrifera and Carpinus pubescens seedlings, and the litter treatment with or without the mixture of B. papyrifera and C. pubescens litter leaves. The root morphological traits were analyzed, and nitrogen (N), phosphorus (P), and potassium (K) were measured. The results showed that AM fungus differently affected the root morphological development and nutrition of both competitive plants, significantly promoting B. papyrifera roots in the increase of dry weight, length, volume, surface area, tips, and branches as well as N, P, and K acquisitions regardless of litter addition. However, there was no apparent influence for C. pubescens roots, except for the diameter in the interspecific competition with litter. The root dry weight, length, volume, surface area, and tips of B. papyrifera under two competitive styles were significantly greater than C. pubescens regulated by AM fungus, presenting significant species differences. The responses of the relative competition intensity (RCI) on root morphological and nutritional traits indicated that AM fungus and litter both asymmetrically alleviated more competitive pressure for B. papyrifera than C. pubescens, and the interspecific competition facilitated more root morphological development and nutrition utilization by endowing B. papyrifera root superiority relative to C. pubescens compared with the intraspecific competition. In conclusion, interspecific competition is more beneficial for plant root development and nutrition than intraspecific competition in the presence of AM fungus and litter via asymmetrically alleviating competitive pressure for different plants.